From 50e536186af50af321bded186716749ab6c5fd6e Mon Sep 17 00:00:00 2001
From: ayogasekaram <abinaya.yogasekaram@contractors.roche.com>
Date: Wed, 20 Sep 2023 16:57:33 +0000
Subject: [PATCH 01/11] Update index categories

---
 book/tlg-index.qmd | 252 ++++++++++++++++++++++++++++-----------------
 1 file changed, 155 insertions(+), 97 deletions(-)

diff --git a/book/tlg-index.qmd b/book/tlg-index.qmd
index 51b0c6ca53..a7d87373a0 100644
--- a/book/tlg-index.qmd
+++ b/book/tlg-index.qmd
@@ -8,45 +8,116 @@ toc-depth: 4
 
 ### **Tables**
 
-#### Adverse Events
+#### ADA
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT01 -- Baseline Prevalence and Incidence of Treatment Emergent ADA](./tables/ada/adat01.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT02 -- Summary of Patients with Treatment-Induced ADA](./tables/ada/adat02.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT03 -- Summary of Serum Concentrations at Timepoints Where ADA Samples Were Collected and Analyzed](./tables/ada/adat03.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT04A -- Baseline Prevalence and Incidence of Treatment Emergent NAbs](./tables/ada/adat04a.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT04B -- Baseline Prevalence and Incidence of NAbs](./tables/ada/adat04b.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET01 -- Safety Summary](./tables/adverse-events/aet01.qmd)
+
+#### Adverse Events
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET01_AESI -- Safety Summary (Adverse Events of Special Interest)](./tables/adverse-events/aet01_aesi.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET02 -- Adverse Events](./tables/adverse-events/aet02.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET01 -- Safety Summary](./tables/adverse-events/aet01.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET02_SMQ -- Adverse Events by Standardized MedDRA Query](./tables/adverse-events/aet02_smq.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET03 -- Adverse Events by Greatest Intensity](./tables/adverse-events/aet03.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET02 -- Adverse Events](./tables/adverse-events/aet02.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET04 -- Adverse Events by Highest NCI CTCAE Grade](./tables/adverse-events/aet04.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET03 -- Adverse Events by Greatest Intensity](./tables/adverse-events/aet03.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET04_PI -- Adverse Events Reported in $\geq$ 10% of Patients by Highest NCI CTCAE Grade](./tables/adverse-events/aet04_pi.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET05 -- Adverse Event Rate Adjusted for Patient-Years at Risk -- First Occurrence](./tables/adverse-events/aet05.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET04 -- Adverse Events by Highest NCI CTCAE Grade](./tables/adverse-events/aet04.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET05_ALL -- Adverse Event Rate Adjusted for Patient-Years at Risk -- All Occurrences](./tables/adverse-events/aet05_all.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET06 -- Adverse Events by Baseline Characteristic](./tables/adverse-events/aet06.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET05 -- Adverse Event Rate Adjusted for Patient-Years at Risk -- First Occurrence](./tables/adverse-events/aet05.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET06_SMQ -- Adverse Events by Baseline Characteristic, by SMQ and Preferred Term](./tables/adverse-events/aet06_smq.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET07 -- Adverse Events Resulting in Death](./tables/adverse-events/aet07.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET06 -- Adverse Events by Baseline Characteristic](./tables/adverse-events/aet06.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET09 -- Adverse Events Related to Study Drug](./tables/adverse-events/aet09.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET07 -- Adverse Events Resulting in Death](./tables/adverse-events/aet07.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET09_SMQ -- Adverse Events Related to Study Drug by Standardized MedDRA Query](./tables/adverse-events/aet09_smq.qmd)
 
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET09 -- Adverse Events Related to Study Drug](./tables/adverse-events/aet09.qmd)
+
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET10 -- Most Common ($\geq$ 5%) Adverse Events](./tables/adverse-events/aet10.qmd)
 
 
+#### Concomitant Medications
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT01 -- Concomitant Medications (GNEDrug Legacy Coding)](./tables/con-med/cmt01.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT01A -- Concomitant Medications by Medication Class and Preferred Name](./tables/con-med/cmt01a.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT01B -- Concomitant Medications by Medication Class and Preferred Name](./tables/con-med/cmt01b.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT02_PT -- Concomitant Medications by Preferred Name (WHODrug Coding)](./tables/con-med/cmt02_pt.qmd)
+
+
+#### Deaths
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DTHT01 -- Deaths](./tables/deaths/dtht01.qmd)
+
+
+#### Demography
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DMT01 -- Demographics and Baseline Characteristics](./tables/demography/dmt01.qmd)
+
+
+#### Disclosures
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DISCLOSUREST01 -- Disclosures Outputs](./tables/disclosures/disclosurest01.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EUDRAT01 -- Non-Serious Adverse Events Reported in $\geq$ 5% of Patients in Any Treatment Group -- Patients and Events](./tables/disclosures/eudrat01.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EUDRAT02 -- Serious Adverse Events, Fatal Serious Adverse Events, and Serious Adverse Events Related to Study Medication](./tables/disclosures/eudrat02.qmd)
+
+
+#### Disposition
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DST01 -- Patient Disposition](./tables/disposition/dst01.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PDT01 -- Major Protocol Deviations](./tables/disposition/pdt01.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PDT02 -- Major Protocol Deviations Related to Epidemic/Pandemic](./tables/disposition/pdt02.qmd)
+
+
+#### ECG
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT01 -- ECG Results and Change from Baseline by Visit](./tables/ecg/egt01.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT02 -- ECG Abnormalities (EGT02_1 & EGT02_2)](./tables/ecg/egt02.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT03 -- Shift Table of ECG Interval Data -- Baseline Versus Minimum/Maximum Post-Baseline](./tables/ecg/egt03.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT04 -- Shift Table of Qualitative ECG Assessments](./tables/ecg/egt04.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT05_QTCAT -- ECG Actual Values and Changes from Baseline by Visit](./tables/ecg/egt05_qtcat.qmd)
+
+
 #### Efficacy
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AOVT01 -- ANCOVA for Multiple End Points](./tables/efficacy/aovt01.qmd)
 
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AOVT02 -- ANCOVA with Single End Point and Customized Table](./tables/efficacy/aovt02.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AOVT03 -- ANCOVA with Consideration of Interaction](./tables/efficacy/aovt03.qmd)
+
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CFBT01 -- Efficacy Data and Change from Baseline by Visit](./tables/efficacy/cfbt01.qmd)
 
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMHT01 -- Cochran-Mantel-Haenszel (CMH) Summary](./tables/efficacy/cmht01.qmd)
+
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[COXT01 -- Cox Regression](./tables/efficacy/coxt01.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[COXT02 -- Multivariable Cox Regression](./tables/efficacy/coxt02.qmd)
@@ -59,11 +130,20 @@ toc-depth: 4
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ONCT05 -- Objective Response Rate by Subgroup](./tables/efficacy/onct05.qmd)
 
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RATET01 -- Event Rate Summary for Recurrent Events](./tables/efficacy/ratet01.qmd)
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RBMIT01 -- Tables for RBMI](./tables/efficacy/rbmit01.qmd)
+
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RSPT01 -- Best Overall Response](./tables/efficacy/rspt01.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[TTET01 -- Time-To-Event Summary](./tables/efficacy/ttet01.qmd)
 
 
+#### Exposure
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EXT01 -- Study Drug Exposure Table](./tables/exposure/ext01.qmd)
+
+
 #### Lab Results
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT01 -- Laboratory Test Results and Change from Baseline by Visit](./tables/lab-results/lbt01.qmd)
@@ -84,18 +164,18 @@ toc-depth: 4
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT09 -- Liver Laboratory Tests -- Patients with Elevated Post-Baseline AST or ALT Levels](./tables/lab-results/lbt09.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT10 -- Liver Laboratory Tests -- Patients with Elevated Post-Baseline AST or ALT Levels at Two Consecutive Visits (with Respect to ULN)](./tables/lab-results/lbt10.qmd)
-
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT10_BL -- Liver Laboratory Tests -- Patients with Elevated Post-Baseline AST or ALT Levels at Two Consecutive Visits (with Respect to Baseline)](./tables/lab-results/lbt10_bl.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT11 -- Time to First Increase in Liver Laboratory Test Result Meeting Hy's Law Laboratory Critieria (with Respect to ULN)](./tables/lab-results/lbt11.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT10 -- Liver Laboratory Tests -- Patients with Elevated Post-Baseline AST or ALT Levels at Two Consecutive Visits (with Respect to ULN)](./tables/lab-results/lbt10.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT11_BL -- Time to First Increase in Liver Laboratory Test Result Meeting Hy's Law Laboratory Critieria (with Respect to Baseline)](./tables/lab-results/lbt11_bl.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT12 -- Liver Laboratory Tests by Time on Treatment -- Patients with Elevated Post-Baseline AST or ALT Levels (with Respect to ULN)](./tables/lab-results/lbt12.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT11 -- Time to First Increase in Liver Laboratory Test Result Meeting Hy's Law Laboratory Critieria (with Respect to ULN)](./tables/lab-results/lbt11.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT12_BL -- Liver Laboratory Tests by Time on Treatment -- Patients with Elevated Post-Baseline AST or ALT Levels (with Respect to Baseline)](./tables/lab-results/lbt12_bl.qmd)
 
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT12 -- Liver Laboratory Tests by Time on Treatment -- Patients with Elevated Post-Baseline AST or ALT Levels (with Respect to ULN)](./tables/lab-results/lbt12.qmd)
+
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT13 -- NCI CTCAE Grade Laboratory Abnormalities by Visit and Baseline Grade](./tables/lab-results/lbt13.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT14 -- Laboratory Test Results Shift Table -- Highest NCI CTCAE Grade Post-Baseline by Baseline NCI CTCAE Grade](./tables/lab-results/lbt14.qmd)
@@ -103,17 +183,12 @@ toc-depth: 4
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT15 -- Laboratory Test Shifts to NCI CTCAE Grade 3-4 Post-Baseline](./tables/lab-results/lbt15.qmd)
 
 
-#### Pharmacokinetic
-
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT01 -- Baseline Prevalence and Incidence of Treatment Emergent ADA](./tables/pharmacokinetic/adat01.qmd)
+#### Medical History
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT02 -- Summary of Patients with Treatment-Induced ADA](./tables/pharmacokinetic/adat02.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[MHT01 -- Medical History](./tables/medical-history/mht01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT03 -- Summary of Serum Concentrations at Timepoints Where ADA Samples Were Collected and Analyzed](./tables/pharmacokinetic/adat03.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT04A -- Baseline Prevalence and Incidence of Treatment Emergent NAbs](./tables/pharmacokinetic/adat04a.qmd)
-
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT04B -- Baseline Prevalence and Incidence of NAbs](./tables/pharmacokinetic/adat04b.qmd)
+#### Pharmacokinetic
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PKCT01 -- Summary Concentration Table](./tables/pharmacokinetic/pkct01.qmd)
 
@@ -134,145 +209,136 @@ toc-depth: 4
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PKPT11 -- Pharmacokinetic Parameter Estimated Ratios of Geometric Means and 90% Confidence Intervals for AUC and CMAX](./tables/pharmacokinetic/pkpt11.qmd)
 
 
-#### Safety
+#### Risk Management Plan
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT01 -- Concomitant Medications (GNEDrug Legacy Coding)](./tables/safety/cmt01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT01 -- Duration of Exposure for Risk Management Plan](./tables/rmp/rmpt01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT01A -- Concomitant Medications by Medication Class and Preferred Name](./tables/safety/cmt01a.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT03 -- Extent of Exposure by Age Group and Gender for Risk Management Plan](./tables/rmp/rmpt03.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT01B -- Concomitant Medications by Medication Class and Preferred Name](./tables/safety/cmt01b.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT04 -- Extent of Exposure by Ethnic Origin for Risk Management Plan](./tables/rmp/rmpt04.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT02_PT -- Concomitant Medications by Preferred Name (WHODrug Coding)](./tables/safety/cmt02_pt.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT05 -- Extent of Exposure by Race for Risk Management Plan](./tables/rmp/rmpt05.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DMT01 -- Demographics and Baseline Characteristics](./tables/safety/dmt01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT06 -- Seriousness, Outcomes, Severity, Frequency with 95% CI for Risk Management Plan](./tables/rmp/rmpt06.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DST01 -- Patient Disposition](./tables/safety/dst01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DTHT01 -- Deaths](./tables/safety/dtht01.qmd)
+#### Safety
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT01 -- ECG Results and Change from Baseline by Visit](./tables/safety/egt01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ENTXX -- Enrollment Variants](./tables/safety/enrollment01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT02 -- ECG Abnormalities (EGT02_1 & EGT02_2)](./tables/safety/egt02.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT03 -- Shift Table of ECG Interval Data -- Baseline Versus Minimum/Maximum Post-Baseline](./tables/safety/egt03.qmd)
+#### Vital Signs
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT04 -- Shift Table of Qualitative ECG Assessments](./tables/safety/egt04.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[VST01 -- Vital Sign Results and Change from Baseline by Visit](./tables/vital-signs/vst01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT05_QTCAT -- ECG Actual Values and Changes from Baseline by Visit](./tables/safety/egt05_qtcat.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[VST02 -- Vital Sign Abnormalities](./tables/vital-signs/vst02.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ENTXX -- Enrollment Variants](./tables/safety/enrollment01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EUDRAT01 -- Non-Serious Adverse Events Reported in $\geq$ 5% of Patients in Any Treatment Group -- Patients and Events](./tables/safety/eudrat01.qmd)
+------------------------------------------------------------------------
+
+### **Listings**
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EUDRAT02 -- Serious Adverse Events, Fatal Serious Adverse Events, and Serious Adverse Events Related to Study Medication](./tables/safety/eudrat02.qmd)
+#### ADA
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EXT01 -- Study Drug Exposure Table](./tables/safety/ext01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAL02 -- Listing of Anti-Drug Antibody Data for Treatment Emergent ADA Positive Patients](./listings/ada/adal02.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[MHT01 -- Medical History](./tables/safety/mht01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PDT01 -- Major Protocol Deviations](./tables/safety/pdt01.qmd)
+#### Adverse Events
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PDT02 -- Major Protocol Deviations Related to Epidemic/Pandemic](./tables/safety/pdt02.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL01_NOLLT -- Listing of Preferred Terms and Investigator-Specified Adverse Event Terms](./listings/adverse-events/ael01_nollt.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[VST01 -- Vital Sign Results and Change from Baseline by Visit](./tables/safety/vst01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL01 -- Listing of Preferred Terms, Lowest Level Terms, and Investigator-Specified Adverse Event Terms](./listings/adverse-events/ael01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[VST02 -- Vital Sign Abnormalities](./tables/safety/vst02.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL02_ED -- Listing of Adverse Events (for Early Development Studies)](./listings/adverse-events/ael02_ed.qmd)
 
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL02 -- Listing of Adverse Events](./listings/adverse-events/ael02.qmd)
 
-#### Other
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL03 -- Listing of Serious Adverse Events](./listings/adverse-events/ael03.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AOVT02 -- ANCOVA with Single End Point and Customized Table](./tables/other/aovt02.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL04 -- Listing of Patient Deaths](./listings/adverse-events/ael04.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AOVT03 -- ANCOVA with Consideration of Interaction](./tables/other/aovt03.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMHT01 -- Cochran-Mantel-Haenszel (CMH) Summary](./tables/other/cmht01.qmd)
+#### Concomitant Medications
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DISCLOSUREST01 -- Disclosures Outputs](./tables/other/disclosurest01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CML01 -- Listing of Previous and Concomitant Medications](./listings/con-med/cml01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RATET01 -- Event Rate Summary for Recurrent Events](./tables/other/ratet01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CML02A_GL -- Listing of Concomitant Medication Class Level 2, Preferred Name, and Investigator-Specified Terms](./listings/con-med/cml02a_gl.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RBMIT01 -- Tables for RBMI](./tables/other/rbmit01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CML02B_GL -- Listing of Concomitant Medication Class, Preferred Name, and Investigator-Specified Terms](./listings/con-med/cml02b_gl.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT01 -- Duration of Exposure for Risk Management Plan](./tables/other/rmpt01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT03 -- Extent of Exposure by Age Group and Gender for Risk Management Plan](./tables/other/rmpt03.qmd)
+#### Disposition
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT04 -- Extent of Exposure by Ethnic Origin for Risk Management Plan](./tables/other/rmpt04.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DSL01 -- Listing of Patients with Study Drug Withdrawn Due to Adverse Events](./listings/disposition/dsl01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT05 -- Extent of Exposure by Race for Risk Management Plan](./tables/other/rmpt05.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DSL02 -- Listing of Patients Who Discontinued Early from Study](./listings/disposition/dsl02.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT06 -- Seriousness, Outcomes, Severity, Frequency with 95% CI for Risk Management Plan](./tables/other/rmpt06.qmd)
 
+#### Development Safety Update Report
 
-------------------------------------------------------------------------
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DSUR4 -- Listing of Patients Who Died During Reporting Period](./listings/dsur/dsur4.qmd)
 
-### **Listings**
 
-#### Pharmacokinetic
+#### ECG
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAL02 -- Listing of Anti-Drug Antibody Data for Treatment Emergent ADA Positive Patients](./listings/pharmacokinetic/adal02.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGL01 -- 'Listing of ECG Data: Safety-Evaluable Patients'](./listings/ecg/egl01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PKCL01 -- Listing of Drug A Concentration by Treatment Group, Patient and Nominal Time](./listings/pharmacokinetic/pkcl01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PKCL02 -- Listing of Drug A Urine Concentration and Volumes](./listings/pharmacokinetic/pkcl02.qmd)
+#### Exposure
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PKPL01 -- Listing of Drug A Plasma PK Parameters](./listings/pharmacokinetic/pkpl01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EXL01 -- Listing of Exposure to Study Drug](./listings/exposure/exl01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PKPL02 -- Listing of Drug A Urine PK Parameters](./listings/pharmacokinetic/pkpl02.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PKPL04 -- Listing of Individual Drug A AUCIFO and CMAX Ratios Following Drug A or Drug B](./listings/pharmacokinetic/pkpl04.qmd)
+#### Lab Results
 
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL01_RLS -- Listing of Laboratory Test Results Using Roche Safety Lab Standardization](./listings/lab-results/lbl01_rls.qmd)
 
-#### Other
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL01 -- Listing of Laboratory Test Results](./listings/lab-results/lbl01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL01 -- Listing of Preferred Terms, Lowest Level Terms, and Investigator-Specified Adverse Event Terms](./listings/other/ael01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL02A_RLS -- Listing of Laboratory Abnormalities Defined by Roche Safety Lab Standardization](./listings/lab-results/lbl02a_rls.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL01_NOLLT -- Listing of Preferred Terms and Investigator-Specified Adverse Event Terms](./listings/other/ael01_nollt.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL02A -- Listing of Laboratory Abnormalities (constant units)](./listings/lab-results/lbl02a.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL02 -- Listing of Adverse Events](./listings/other/ael02.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL02B -- Listing of Laboratory Abnormalities (variable units)](./listings/lab-results/lbl02b.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL02_ED -- Listing of Adverse Events (for Early Development Studies)](./listings/other/ael02_ed.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL03 -- Listing of Serious Adverse Events](./listings/other/ael03.qmd)
+#### Medical History
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL04 -- Listing of Patient Deaths](./listings/other/ael04.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[MHL01 -- Listing of Medical History and Concurrent Diseases](./listings/medical-history/mhl01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CML01 -- Listing of Previous and Concomitant Medications](./listings/other/cml01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CML02A_GL -- Listing of Concomitant Medication Class Level 2, Preferred Name, and Investigator-Specified Terms](./listings/other/cml02a_gl.qmd)
+#### Pharmacokinetic
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CML02B_GL -- Listing of Concomitant Medication Class, Preferred Name, and Investigator-Specified Terms](./listings/other/cml02b_gl.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PKCL01 -- Listing of Drug A Concentration by Treatment Group, Patient and Nominal Time](./listings/pharmacokinetic/pkcl01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DSL01 -- Listing of Patients with Study Drug Withdrawn Due to Adverse Events](./listings/other/dsl01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PKCL02 -- Listing of Drug A Urine Concentration and Volumes](./listings/pharmacokinetic/pkcl02.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DSL02 -- Listing of Patients Who Discontinued Early from Study](./listings/other/dsl02.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PKPL01 -- Listing of Drug A Plasma PK Parameters](./listings/pharmacokinetic/pkpl01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DSUR4 -- Listing of Patients Who Died During Reporting Period](./listings/other/dsur4.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PKPL02 -- Listing of Drug A Urine PK Parameters](./listings/pharmacokinetic/pkpl02.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGL01 -- 'Listing of ECG Data: Safety-Evaluable Patients'](./listings/other/egl01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[PKPL04 -- Listing of Individual Drug A AUCIFO and CMAX Ratios Following Drug A or Drug B](./listings/pharmacokinetic/pkpl04.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EXL01 -- Listing of Exposure to Study Drug](./listings/other/exl01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL01 -- Listing of Laboratory Test Results](./listings/other/lbl01.qmd)
+#### Vital Signs
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL01_RLS -- Listing of Laboratory Test Results Using Roche Safety Lab Standardization](./listings/other/lbl01_rls.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[VSL01 -- 'Listing of Vital Signs: Safety-Evaluable Patients'](./listings/vital-signs/vsl01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL02A -- Listing of Laboratory Abnormalities (constant units)](./listings/other/lbl02a.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL02A_RLS -- Listing of Laboratory Abnormalities Defined by Roche Safety Lab Standardization](./listings/other/lbl02a_rls.qmd)
+------------------------------------------------------------------------
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL02B -- Listing of Laboratory Abnormalities (variable units)](./listings/other/lbl02b.qmd)
+### **Graphs**
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[MHL01 -- Listing of Medical History and Concurrent Diseases](./listings/other/mhl01.qmd)
+#### Efficacy
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ONCL01 -- Listing of Individual Efficacy Data](./listings/other/oncl01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[FSTG01 -- Subgroup Analysis of Best Overall Response](./graphs/efficacy/fstg01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[VSL01 -- 'Listing of Vital Signs: Safety-Evaluable Patients'](./listings/other/vsl01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[FSTG02 -- Subgroup Analysis of Survival Duration](./graphs/efficacy/fstg02.qmd)
 
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[MMRMG01 -- Plots for Mixed-Effect Model Repeated Measures Analysis](./graphs/efficacy/mmrmg01.qmd)
 
-------------------------------------------------------------------------
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[MMRMG02 -- Forest Plot for Mixed-Effect Model Repeated Measures](./graphs/efficacy/mmrmg02.qmd)
 
-### **Graphs**
 
 #### Pharmacokinetic
 
@@ -301,19 +367,11 @@ toc-depth: 4
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CIG01 -- Confidence Interval Plot](./graphs/other/cig01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[FSTG01 -- Subgroup Analysis of Best Overall Response](./graphs/other/fstg01.qmd)
-
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[FSTG02 -- Subgroup Analysis of Survival Duration](./graphs/other/fstg02.qmd)
-
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[IPPG01 -- Individual Patient Plot Over Time](./graphs/other/ippg01.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[KMG01 -- Kaplan-Meier Plot](./graphs/other/kmg01.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LTG01 -- Lattice Plot of Laboratory Tests by Treatment Group Over Time](./graphs/other/ltg01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[MMRMG01 -- Plots for Mixed-Effect Model Repeated Measures Analysis](./graphs/other/mmrmg01.qmd)
-
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[MMRMG02 -- Forest Plot for Mixed-Effect Model Repeated Measures](./graphs/other/mmrmg02.qmd)
-
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[MNG01 -- Mean Plot](./graphs/other/mng01.qmd)
 

From 84714b1ab559e7d9ef7184b17b37fb3e5863d9c5 Mon Sep 17 00:00:00 2001
From: ayogasekaram <abinaya.yogasekaram@contractors.roche.com>
Date: Wed, 20 Sep 2023 18:04:57 +0000
Subject: [PATCH 02/11] update folder names for sidebar

---
 book/generate-index.R                         |  9 ++++---
 .../cml01.qmd                                 |  0
 .../cml02a_gl.qmd                             |  0
 .../cml02b_gl.qmd                             |  0
 .../dsur4.qmd                                 |  0
 .../cmt01.qmd                                 |  0
 .../cmt01a.qmd                                |  0
 .../cmt01b.qmd                                |  0
 .../cmt02_pt.qmd                              |  0
 .../{rmp => risk-management-plan}/rmpt01.qmd  |  0
 .../{rmp => risk-management-plan}/rmpt03.qmd  |  0
 .../{rmp => risk-management-plan}/rmpt04.qmd  |  0
 .../{rmp => risk-management-plan}/rmpt05.qmd  |  0
 .../{rmp => risk-management-plan}/rmpt06.qmd  |  0
 book/tlg-index.qmd                            | 26 +++++++++----------
 15 files changed, 18 insertions(+), 17 deletions(-)
 rename book/listings/{con-med => concomitant-medications}/cml01.qmd (100%)
 rename book/listings/{con-med => concomitant-medications}/cml02a_gl.qmd (100%)
 rename book/listings/{con-med => concomitant-medications}/cml02b_gl.qmd (100%)
 rename book/listings/{dsur => development-safety-update-report}/dsur4.qmd (100%)
 rename book/tables/{con-med => concomitant-medications}/cmt01.qmd (100%)
 rename book/tables/{con-med => concomitant-medications}/cmt01a.qmd (100%)
 rename book/tables/{con-med => concomitant-medications}/cmt01b.qmd (100%)
 rename book/tables/{con-med => concomitant-medications}/cmt02_pt.qmd (100%)
 rename book/tables/{rmp => risk-management-plan}/rmpt01.qmd (100%)
 rename book/tables/{rmp => risk-management-plan}/rmpt03.qmd (100%)
 rename book/tables/{rmp => risk-management-plan}/rmpt04.qmd (100%)
 rename book/tables/{rmp => risk-management-plan}/rmpt05.qmd (100%)
 rename book/tables/{rmp => risk-management-plan}/rmpt06.qmd (100%)

diff --git a/book/generate-index.R b/book/generate-index.R
index 2fd8e3c71a..20473e2308 100755
--- a/book/generate-index.R
+++ b/book/generate-index.R
@@ -39,7 +39,7 @@ cat(
 section_header("Tables")
 create_subsection("./tables/ada", "ADA")
 create_subsection("./tables/adverse-events", "Adverse Events")
-create_subsection("./tables/con-med", "Concomitant Medications")
+create_subsection("./tables/concomitant-medications", "Concomitant Medications")
 create_subsection("./tables/deaths", "Deaths")
 create_subsection("./tables/demography", "Demography")
 create_subsection("./tables/disclosures", "Disclosures")
@@ -50,7 +50,7 @@ create_subsection("./tables/exposure", "Exposure")
 create_subsection("./tables/lab-results", "Lab Results")
 create_subsection("./tables/medical-history", "Medical History")
 create_subsection("./tables/pharmacokinetic", "Pharmacokinetic")
-create_subsection("./tables/rmp", "Risk Management Plan")
+create_subsection("./tables/risk-management-plan", "Risk Management Plan")
 create_subsection("./tables/safety", "Safety")
 create_subsection("./tables/vital-signs", "Vital Signs")
 
@@ -59,9 +59,9 @@ create_subsection("./tables/vital-signs", "Vital Signs")
 section_header("Listings")
 create_subsection("./listings/ada", "ADA")
 create_subsection("./listings/adverse-events", "Adverse Events")
-create_subsection("./listings/con-med", "Concomitant Medications")
+create_subsection("./listings/concomitant-medications", "Concomitant Medications")
 create_subsection("./listings/disposition", "Disposition")
-create_subsection("./listings/dsur", "Development Safety Update Report")
+create_subsection("./listings/development-safety-update-report", "Development Safety Update Report")
 create_subsection("./listings/ecg", "ECG")
 create_subsection("./listings/exposure", "Exposure")
 create_subsection("./listings/lab-results", "Lab Results")
@@ -75,3 +75,4 @@ section_header("Graphs")
 create_subsection("./graphs/efficacy", "Efficacy")
 create_subsection("./graphs/pharmacokinetic", "Pharmacokinetic")
 create_subsection("./graphs/other", "Other")
+
diff --git a/book/listings/con-med/cml01.qmd b/book/listings/concomitant-medications/cml01.qmd
similarity index 100%
rename from book/listings/con-med/cml01.qmd
rename to book/listings/concomitant-medications/cml01.qmd
diff --git a/book/listings/con-med/cml02a_gl.qmd b/book/listings/concomitant-medications/cml02a_gl.qmd
similarity index 100%
rename from book/listings/con-med/cml02a_gl.qmd
rename to book/listings/concomitant-medications/cml02a_gl.qmd
diff --git a/book/listings/con-med/cml02b_gl.qmd b/book/listings/concomitant-medications/cml02b_gl.qmd
similarity index 100%
rename from book/listings/con-med/cml02b_gl.qmd
rename to book/listings/concomitant-medications/cml02b_gl.qmd
diff --git a/book/listings/dsur/dsur4.qmd b/book/listings/development-safety-update-report/dsur4.qmd
similarity index 100%
rename from book/listings/dsur/dsur4.qmd
rename to book/listings/development-safety-update-report/dsur4.qmd
diff --git a/book/tables/con-med/cmt01.qmd b/book/tables/concomitant-medications/cmt01.qmd
similarity index 100%
rename from book/tables/con-med/cmt01.qmd
rename to book/tables/concomitant-medications/cmt01.qmd
diff --git a/book/tables/con-med/cmt01a.qmd b/book/tables/concomitant-medications/cmt01a.qmd
similarity index 100%
rename from book/tables/con-med/cmt01a.qmd
rename to book/tables/concomitant-medications/cmt01a.qmd
diff --git a/book/tables/con-med/cmt01b.qmd b/book/tables/concomitant-medications/cmt01b.qmd
similarity index 100%
rename from book/tables/con-med/cmt01b.qmd
rename to book/tables/concomitant-medications/cmt01b.qmd
diff --git a/book/tables/con-med/cmt02_pt.qmd b/book/tables/concomitant-medications/cmt02_pt.qmd
similarity index 100%
rename from book/tables/con-med/cmt02_pt.qmd
rename to book/tables/concomitant-medications/cmt02_pt.qmd
diff --git a/book/tables/rmp/rmpt01.qmd b/book/tables/risk-management-plan/rmpt01.qmd
similarity index 100%
rename from book/tables/rmp/rmpt01.qmd
rename to book/tables/risk-management-plan/rmpt01.qmd
diff --git a/book/tables/rmp/rmpt03.qmd b/book/tables/risk-management-plan/rmpt03.qmd
similarity index 100%
rename from book/tables/rmp/rmpt03.qmd
rename to book/tables/risk-management-plan/rmpt03.qmd
diff --git a/book/tables/rmp/rmpt04.qmd b/book/tables/risk-management-plan/rmpt04.qmd
similarity index 100%
rename from book/tables/rmp/rmpt04.qmd
rename to book/tables/risk-management-plan/rmpt04.qmd
diff --git a/book/tables/rmp/rmpt05.qmd b/book/tables/risk-management-plan/rmpt05.qmd
similarity index 100%
rename from book/tables/rmp/rmpt05.qmd
rename to book/tables/risk-management-plan/rmpt05.qmd
diff --git a/book/tables/rmp/rmpt06.qmd b/book/tables/risk-management-plan/rmpt06.qmd
similarity index 100%
rename from book/tables/rmp/rmpt06.qmd
rename to book/tables/risk-management-plan/rmpt06.qmd
diff --git a/book/tlg-index.qmd b/book/tlg-index.qmd
index a7d87373a0..19a704936b 100644
--- a/book/tlg-index.qmd
+++ b/book/tlg-index.qmd
@@ -56,13 +56,13 @@ toc-depth: 4
 
 #### Concomitant Medications
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT01 -- Concomitant Medications (GNEDrug Legacy Coding)](./tables/con-med/cmt01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT01 -- Concomitant Medications (GNEDrug Legacy Coding)](./tables/concomitant-medications/cmt01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT01A -- Concomitant Medications by Medication Class and Preferred Name](./tables/con-med/cmt01a.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT01A -- Concomitant Medications by Medication Class and Preferred Name](./tables/concomitant-medications/cmt01a.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT01B -- Concomitant Medications by Medication Class and Preferred Name](./tables/con-med/cmt01b.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT01B -- Concomitant Medications by Medication Class and Preferred Name](./tables/concomitant-medications/cmt01b.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT02_PT -- Concomitant Medications by Preferred Name (WHODrug Coding)](./tables/con-med/cmt02_pt.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CMT02_PT -- Concomitant Medications by Preferred Name (WHODrug Coding)](./tables/concomitant-medications/cmt02_pt.qmd)
 
 
 #### Deaths
@@ -211,15 +211,15 @@ toc-depth: 4
 
 #### Risk Management Plan
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT01 -- Duration of Exposure for Risk Management Plan](./tables/rmp/rmpt01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT01 -- Duration of Exposure for Risk Management Plan](./tables/risk-management-plan/rmpt01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT03 -- Extent of Exposure by Age Group and Gender for Risk Management Plan](./tables/rmp/rmpt03.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT03 -- Extent of Exposure by Age Group and Gender for Risk Management Plan](./tables/risk-management-plan/rmpt03.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT04 -- Extent of Exposure by Ethnic Origin for Risk Management Plan](./tables/rmp/rmpt04.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT04 -- Extent of Exposure by Ethnic Origin for Risk Management Plan](./tables/risk-management-plan/rmpt04.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT05 -- Extent of Exposure by Race for Risk Management Plan](./tables/rmp/rmpt05.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT05 -- Extent of Exposure by Race for Risk Management Plan](./tables/risk-management-plan/rmpt05.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT06 -- Seriousness, Outcomes, Severity, Frequency with 95% CI for Risk Management Plan](./tables/rmp/rmpt06.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[RMPT06 -- Seriousness, Outcomes, Severity, Frequency with 95% CI for Risk Management Plan](./tables/risk-management-plan/rmpt06.qmd)
 
 
 #### Safety
@@ -260,11 +260,11 @@ toc-depth: 4
 
 #### Concomitant Medications
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CML01 -- Listing of Previous and Concomitant Medications](./listings/con-med/cml01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CML01 -- Listing of Previous and Concomitant Medications](./listings/concomitant-medications/cml01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CML02A_GL -- Listing of Concomitant Medication Class Level 2, Preferred Name, and Investigator-Specified Terms](./listings/con-med/cml02a_gl.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CML02A_GL -- Listing of Concomitant Medication Class Level 2, Preferred Name, and Investigator-Specified Terms](./listings/concomitant-medications/cml02a_gl.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CML02B_GL -- Listing of Concomitant Medication Class, Preferred Name, and Investigator-Specified Terms](./listings/con-med/cml02b_gl.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[CML02B_GL -- Listing of Concomitant Medication Class, Preferred Name, and Investigator-Specified Terms](./listings/concomitant-medications/cml02b_gl.qmd)
 
 
 #### Disposition
@@ -276,7 +276,7 @@ toc-depth: 4
 
 #### Development Safety Update Report
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DSUR4 -- Listing of Patients Who Died During Reporting Period](./listings/dsur/dsur4.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[DSUR4 -- Listing of Patients Who Died During Reporting Period](./listings/development-safety-update-report/dsur4.qmd)
 
 
 #### ECG

From 850d24bcd0d71b61543879d4919655973243ad4b Mon Sep 17 00:00:00 2001
From: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
Date: Wed, 20 Sep 2023 18:13:43 +0000
Subject: [PATCH 03/11] [skip actions] Restyle files

---
 book/generate-index.R | 1 -
 1 file changed, 1 deletion(-)

diff --git a/book/generate-index.R b/book/generate-index.R
index 20473e2308..99cec8b787 100755
--- a/book/generate-index.R
+++ b/book/generate-index.R
@@ -75,4 +75,3 @@ section_header("Graphs")
 create_subsection("./graphs/efficacy", "Efficacy")
 create_subsection("./graphs/pharmacokinetic", "Pharmacokinetic")
 create_subsection("./graphs/other", "Other")
-

From d7c151e764f644bc04013661e0d2cc841d387997 Mon Sep 17 00:00:00 2001
From: ayogasekaram <abinaya.yogasekaram@contractors.roche.com>
Date: Thu, 21 Sep 2023 15:39:17 +0000
Subject: [PATCH 04/11] update folders to uppercase.

---
 book/listings/ADA/adal02.qmd    | 131 ++++++++++++++++
 book/listings/ECG/egl01.qmd     |  89 +++++++++++
 book/tables/ADA/adat01.qmd      | 230 ++++++++++++++++++++++++++++
 book/tables/ADA/adat02.qmd      | 151 +++++++++++++++++++
 book/tables/ADA/adat03.qmd      | 131 ++++++++++++++++
 book/tables/ADA/adat04a.qmd     | 240 ++++++++++++++++++++++++++++++
 book/tables/ADA/adat04b.qmd     | 223 ++++++++++++++++++++++++++++
 book/tables/ECG/egt01.qmd       | 142 ++++++++++++++++++
 book/tables/ECG/egt02.qmd       | 126 ++++++++++++++++
 book/tables/ECG/egt03.qmd       | 204 +++++++++++++++++++++++++
 book/tables/ECG/egt04.qmd       | 100 +++++++++++++
 book/tables/ECG/egt05_qtcat.qmd | 255 ++++++++++++++++++++++++++++++++
 12 files changed, 2022 insertions(+)
 create mode 100644 book/listings/ADA/adal02.qmd
 create mode 100644 book/listings/ECG/egl01.qmd
 create mode 100644 book/tables/ADA/adat01.qmd
 create mode 100644 book/tables/ADA/adat02.qmd
 create mode 100644 book/tables/ADA/adat03.qmd
 create mode 100644 book/tables/ADA/adat04a.qmd
 create mode 100644 book/tables/ADA/adat04b.qmd
 create mode 100644 book/tables/ECG/egt01.qmd
 create mode 100644 book/tables/ECG/egt02.qmd
 create mode 100644 book/tables/ECG/egt03.qmd
 create mode 100644 book/tables/ECG/egt04.qmd
 create mode 100644 book/tables/ECG/egt05_qtcat.qmd

diff --git a/book/listings/ADA/adal02.qmd b/book/listings/ADA/adal02.qmd
new file mode 100644
index 0000000000..9ddcaf7f56
--- /dev/null
+++ b/book/listings/ADA/adal02.qmd
@@ -0,0 +1,131 @@
+---
+title: ADAL02
+subtitle: Listing of Anti-Drug Antibody Data for Treatment Emergent ADA Positive Patients
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(dplyr)
+library(rlistings)
+library(scda)
+
+adab <- synthetic_cdisc_dataset("latest", "adab") %>%
+  filter(NFRLT %% 1 == 0 & NFRLT > 0)
+
+trt <- "A: Drug X"
+drug_a <- "A: Drug X Antibody"
+drugcd <- unique(adab$PARAMCD[adab$PARAM == "Antibody titer units"])[1]
+min_titer <- 1.10
+
+adab_x <- adab %>%
+  filter(
+    ARM == trt,
+    PARCAT1 == drug_a,
+    ADPBLPFL == "Y"
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %>%
+  unique() %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  filter(if_any(matches("Treatment Emergent - Positive"), ~ .x == 1)) %>%
+  # filter(`Treatment Emergent - Positive` == 1) %>%
+  mutate(
+    VISN = factor(paste0(
+      VISIT, "\n(Day ",
+      ifelse(
+        NFRLT %% 1 == 0,
+        NFRLT,
+        as.character(format(round(NFRLT, 2), nsmall = 2))
+      ),
+      ")"
+    )),
+    PTES = ifelse(
+      ifelse("Treatment induced ADA" %in% names(.), `Treatment induced ADA` == 1, F),
+      ifelse(
+        "Transient ADA" %in% names(.) & `Transient ADA` == 1,
+        "Induced (Transient)",
+        "Induced (Persistent)"
+      ),
+      "Enhanced"
+    )
+  ) %>%
+  mutate(
+    AVAL = paste0(
+      ifelse(
+        ifelse("ADA interpreted per sample result" %in% names(.), `ADA interpreted per sample result` == 0, F),
+        "NEGATIVE",
+        ifelse(
+          ifelse("Antibody titer units" %in% names(.), !is.na(`Antibody titer units`), F),
+          ifelse(
+            `Antibody titer units` < min_titer,
+            paste0("<", format(min_titer, nsmall = 2)),
+            as.character(format(round(`Antibody titer units`, 2), nsmall = 2))
+          ),
+          "---"
+        )
+      ),
+      ifelse(
+        ifelse("NAB interpreted per sample result" %in% names(.), `NAB interpreted per sample result` == 1, F),
+        "*",
+        ""
+      )
+    )
+  )
+
+out <- adab_x %>%
+  select(USUBJID, VISN, AVAL, PTES) %>%
+  tidyr::pivot_wider(
+    names_from = VISN,
+    values_from = AVAL
+  ) %>%
+  select(USUBJID, unique(adab_x$VISN[order(adab_x$NFRLT)]), PTES)
+
+var_labels(out) <- names(out)
+
+out <- out %>%
+  var_relabel(
+    USUBJID = "Subject ID",
+    PTES = "Patient Treatment\nEmergent ADA Status"
+  )
+```
+
+## Standard Listing
+
+```{r lsting, test = list(lsting = "lsting")}
+lsting <- as_listing(
+  out,
+  key_cols = "USUBJID",
+  disp_cols = names(out),
+  main_title = paste0(
+    "Listing of Anti-", drugcd, " Antibody Data for Treatment Emergent ADA Positive Patients, PK Population",
+    "\nProtocol: ", unique(adab$PARCAT1)[1]
+  ),
+  subtitles = paste("\nTreatment Group:", trt),
+  main_footer = "Minimum reportable titer = 1.10 (example only)
+--- = No sample evaluated
+ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies)
+Number of patients positive for Treatment Emergent ADA = the number of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.
+Treatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.
+Treatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
+Transient ADA = ADA positive result detected (a) at only one post-baseline sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints during treatment where the first and last ADA positive samples  are separated by a period of < 16 weeks, irrespective of any negative samples in between.
+Persistent ADA =  ADA positive result detected (a) at the last post-baseline sampling timepoint, OR (b) at 2 or more time points during treatment where the first and last ADA positive samples are separated by a period ≥ 16 weeks, irrespective of any negative samples in between.
+Asterisk denotes sample that tested positive for Neutralizing Antibodies."
+)
+
+head(lsting, 20)
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/listings/ECG/egl01.qmd b/book/listings/ECG/egl01.qmd
new file mode 100644
index 0000000000..a595b864c9
--- /dev/null
+++ b/book/listings/ECG/egl01.qmd
@@ -0,0 +1,89 @@
+---
+title: EGL01
+subtitle: 'Listing of ECG Data: Safety-Evaluable Patients'
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(dplyr)
+library(scda)
+library(rlistings)
+
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+get_param_unit_range <- function(dataset) {
+  u_rng <- lapply(unique(dataset$PARAMCD), function(p) {
+    dat <- dataset %>% filter(PARAMCD == p)
+    list(
+      unit = unique(dat$AVALU),
+      range = paste0(unique(dat$ANRLO), "-", unique(dat$ANRHI))
+    )
+  })
+  names(u_rng) <- unique(dataset$PARAMCD)
+  u_rng
+}
+
+eg_u_rng <- get_param_unit_range(adeg)
+
+adeg_sub <- adeg %>%
+  filter(!is.na(AVAL) & SAFFL == "Y" & ANL01FL == "Y" & !is.na(EGSEQ) & PARAMCD != "ECGINTP") %>%
+  mutate(
+    CRTNPT = paste(SITEID, sub("^.*-([[:alnum:]]+)$", "\\1", SUBJID), sep = "/"),
+    AGSXRC = paste(AGE, SEX, RACE, sep = "/"),
+    AVAL = format(round(AVAL, 2), nsmall = 2),
+    AVAL_ANRIND = ifelse(ANRIND %in% c("NORMAL", ""), AVAL, paste(AVAL, substr(ANRIND, 1, 1), sep = "/")),
+    CHG = format(round(CHG, 2), nsmall = 2)
+  )
+
+anl_eg <- adeg_sub %>%
+  select(SUBJID, CRTNPT, AGSXRC, TRT01A, PARAMCD, AVAL_ANRIND, CHG, ADY, AVISIT, ADTM) %>%
+  tidyr::pivot_wider(
+    id_cols = c(SUBJID, CRTNPT, AGSXRC, TRT01A, ADY, AVISIT, ADTM),
+    names_from = PARAMCD,
+    values_from = c(AVAL_ANRIND, CHG)
+  )
+
+out <- anl_eg %>%
+  select(CRTNPT, AGSXRC, TRT01A, AVISIT, ADY, AVAL_ANRIND_HR, CHG_HR, AVAL_ANRIND_QT, CHG_QT, AVAL_ANRIND_RR, CHG_RR) %>%
+  var_relabel(
+    CRTNPT = "Center/Subject ID",
+    AGSXRC = "Age/Sex/Race",
+    TRT01A = "Treatment",
+    AVISIT = "Visit",
+    ADY = "Study\nDay",
+    AVAL_ANRIND_HR = paste0("Heart Rate Result\n(", eg_u_rng$HR$unit, ");\nRange:(", eg_u_rng$HR$range, ")"),
+    CHG_HR = "Heart Rate\nChange from BL",
+    AVAL_ANRIND_QT = paste0("QT Duration Result\n(", eg_u_rng$QT$unit, ");\nRange:(", eg_u_rng$QT$range, ")"),
+    CHG_QT = "QT Duration\nChange from BL",
+    AVAL_ANRIND_RR = paste0("RR Duration Result\n(", eg_u_rng$RR$unit, ");\nRange:(", eg_u_rng$RR$range, ")"),
+    CHG_RR = "RR Duration\nChange from BL"
+  )
+```
+
+## Standard Listing
+
+```{r lsting, test = list(lsting = "lsting")}
+lsting <- as_listing(
+  out,
+  key_cols = c("CRTNPT", "AGSXRC", "TRT01A", "AVISIT", "ADY"),
+  disp_cols = names(out),
+  main_title = "Listing of ECG Data: Safety-Evaluable Patients",
+  main_footer = "Baseline is the patient's last observation prior to initiation of study drug. Abnormalities are flagged as high (H) or low (L) if
+  outside the Roche standard reference range."
+)
+
+head(lsting, 20)
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ADA/adat01.qmd b/book/tables/ADA/adat01.qmd
new file mode 100644
index 0000000000..31a13bd819
--- /dev/null
+++ b/book/tables/ADA/adat01.qmd
@@ -0,0 +1,230 @@
+---
+title: ADAT01
+subtitle: Baseline Prevalence and Incidence of Treatment Emergent ADA
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(tern)
+library(dplyr)
+library(scda)
+library(tibble)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adab <- synthetic_cdisc_dataset("latest", "adab")
+
+combodf <- tribble(
+  ~valname, ~label, ~levelcombo, ~exargs,
+  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
+  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
+)
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adab <- adab %>%
+  filter(PARCAT1 == "A: Drug X Antibody") %>%
+  select(-ARRLT, -NRRLT)
+
+# Baseline Pts
+adab_b <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL == "Y",
+    ADABLPFL == "Y",
+    PARAM %in% c("ADA interpreted per sample result")
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU) %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  mutate_at(
+    c("ADA interpreted per sample result"),
+    as.logical
+  ) %>%
+  mutate(
+    ADABLPFL = ADABLPFL == "Y",
+    PADABLPFL = `ADA interpreted per sample result` == "TRUE",
+    NADABLPFL = `ADA interpreted per sample result` == "FALSE"
+  ) %>%
+  var_relabel(
+    ADABLPFL = "Baseline evaluable patients",
+    PADABLPFL = "Patient with a positive sample at baseline",
+    NADABLPFL = "Patient with no positive samples at baseline"
+  )
+
+# Post Baseline Treatment Enhanced NAb positive Pts
+adab_pb <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL != "Y",
+    ADPBLPFL == "Y",
+    PARAM %in% c(
+      "ADA interpreted per sample result",
+      "Treatment Emergent - Positive",
+      "Treatment induced ADA",
+      "Treatment enhanced ADA",
+      "Treatment Emergent - Negative",
+      "Treatment unaffected"
+    )
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU) %>%
+  unique() %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  mutate(
+    across(
+      any_of(c(
+        "ADA interpreted per sample result", "Treatment Emergent - Positive",
+        "Treatment induced ADA", "Treatment enhanced ADA",
+        "Treatment Emergent - Negative", "Treatment unaffected"
+      )),
+      as.logical
+    )
+  ) %>%
+  mutate(
+    ADPBLPFL = ADPBLPFL == "Y",
+    PTEFL = if (exists("Treatment Emergent - Positive", where = .)) `Treatment Emergent - Positive` == "TRUE" else FALSE,
+    TIFL = if (exists("Treatment induced ADA", where = .)) `Treatment induced ADA` == "TRUE" else FALSE,
+    TEFL = if (exists("Treatment enhanced ADA", where = .)) `Treatment enhanced ADA` == "TRUE" else FALSE,
+    NTEFL = if (exists("Treatment Emergent - Negative", where = .)) `Treatment Emergent - Negative` == "TRUE" else FALSE,
+    TUFL = if (exists("Treatment unaffected", where = .)) `Treatment unaffected` == "TRUE" else FALSE
+  ) %>%
+  var_relabel(
+    ADPBLPFL = "Post-baseline evaluable patients",
+    PTEFL = "Patient positive for Treatment Emergent ADA",
+    TIFL = "Treatment-induced ADA",
+    TEFL = "Treatment-enhanced ADA",
+    NTEFL = "Patient negative for Treatment Emergent ADA",
+    TUFL = "Treatment unaffected"
+  )
+```
+
+## Standard Table
+
+```{r variant1, test = list(result_v1 = "result")}
+# Layout for Baseline Prevalence of NAbs
+lyt_bl <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = add_combo_levels(combodf)
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "ADABLPFL",
+    .stats = "count",
+    var_labels = "Baseline Prevalence of ADAs",
+    show_labels = "visible",
+    table_names = "t1"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "PADABLPFL",
+    table_names = "t2",
+    .indent_mods = 1L,
+    var_labels = "a",
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NADABLPFL",
+    .stats = "count",
+    show_labels = "hidden",
+    .indent_mods = 1L,
+    table_names = "t3"
+  )
+
+# Layout for incidence of NAbs
+lyt_pb <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = add_combo_levels(combodf)
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "ADPBLPFL",
+    .stats = "count",
+    var_labels = "Incidence of Treatment Emergent ADAs",
+    show_labels = "visible",
+    table_names = "tb1"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "PTEFL",
+    table_names = "tb2",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("TIFL", "TEFL"),
+    .stats = "count",
+    table_names = "tb3",
+    .indent_mods = 2L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NTEFL",
+    table_names = "tb4",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "TUFL",
+    .stats = "count",
+    table_names = "tb5",
+    .indent_mods = 2L,
+    show_labels = "hidden"
+  )
+
+result_1 <- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)
+result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
+
+# Combine tables.
+result_1@col_info <- result_2@col_info
+result <- rbind(result_1, result_2)
+
+# Change the column order.
+result <- cbind_rtables(result[, 1], result[, 3]) %>%
+  cbind_rtables(result[, 4]) %>%
+  cbind_rtables(result[, 2]) %>%
+  cbind_rtables(result[, 5])
+
+main_title(result) <- paste(
+  "Baseline Prevalence and Incidence of Treatment Emergent ADA"
+)
+main_footer(result) <- paste(
+  "ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient = a patient with an ADA assay result from a baseline sample(s)
+  Post-baseline evaluable patient = a patient with an ADA assay result from at least one post-baseline sample Number of patients positive for Treatment Emergent
+  ADA = the number (and percentage) of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.
+  Treatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.
+  Treatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
+  Number of patients negative for Treatment Emergent ADA = number of post-baseline evaluable patients with negative or missing baseline ADA result(s) and all negative post-baseline results, or a patient who is treatment unaffected.
+  Treatment unaffected = A post-baseline evaluable patient with a positive ADA result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing.
+  For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer."
+)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| code-fold: show
+
+# In progress
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ADA/adat02.qmd b/book/tables/ADA/adat02.qmd
new file mode 100644
index 0000000000..630e8e817e
--- /dev/null
+++ b/book/tables/ADA/adat02.qmd
@@ -0,0 +1,151 @@
+---
+title: ADAT02
+subtitle: Summary of Patients with Treatment-Induced ADA
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(tern)
+library(dplyr)
+library(scda)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adab <- synthetic_cdisc_dataset("latest", "adab")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adab <- df_explicit_na(adab) %>%
+  mutate(ADPBLPFL = "Y") %>% # temp fix
+  filter(
+    ADPBLPFL == "Y",
+    !PARAM %in% c(
+      "NAB interpreted per sample result",
+      "NAB Status of a patient",
+      "Treatment enhanced ADA"
+    )
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %>%
+  unique() %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  mutate(
+    across(
+      any_of(c(
+        "Treatment induced ADA",
+        "Transient ADA",
+        "Persistent ADA"
+      )),
+      as.logical
+    )
+  ) %>%
+  mutate(
+    ADPBLPFL = ADPBLPFL == "Y",
+    TI_ADA = if (exists("Treatment induced ADA", where = .)) `Treatment induced ADA` else FALSE
+  ) %>%
+  var_relabel(
+    ADPBLPFL = "Post-baseline evaluable patients",
+    TI_ADA = "Treatment-induced ADA patients"
+  )
+
+adab_ti <- adab %>%
+  filter(TI_ADA)
+```
+
+## Standard Table
+
+```{r variant1, test = list(result_v1 = "result")}
+# Layout for post-baseline evaluable patient variables from adab dataset.
+lyt_adab <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = drop_split_levels
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "ADPBLPFL",
+    .stats = "count",
+    table_names = "post_baseline"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "TI_ADA"
+  )
+
+# Layout for treatment-induced patient variables from adab dataset.
+lyt_adab_ti <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = keep_split_levels(c("A: Drug X", "C: Combination", "<Missing>")) # temp fix
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("Transient ADA", "Persistent ADA"),
+    var_labels = "Treatment-induced ADA patients with",
+    show_labels = "visible"
+  ) %>%
+  analyze_vars(
+    "Time to onset of ADA",
+    .stats = "median",
+    nested = FALSE,
+    .labels = c(median = "Median time to onset of ADA (weeks)")
+  ) %>%
+  analyze_vars(
+    "Antibody titer units",
+    .stats = "range",
+    nested = FALSE,
+    .labels = c(range = "ADA titer range (min - max)")
+  )
+
+result_adab <- build_table(lyt_adab, df = adab, alt_counts_df = adsl)
+result_adab_ti <- build_table(lyt_adab_ti, df = adab_ti, alt_counts_df = adsl)
+
+# Combine tables.
+col_info(result_adab) <- col_info(result_adab_ti)
+result <- rbind(
+  result_adab,
+  result_adab_ti
+)
+
+main_title(result) <- paste(
+  "Summary of Patients with Treatment-Induced ADA, PK Population"
+)
+subtitles(result) <- paste("Protocol:", unique(adab$PARCAT1)[1])
+main_footer(result) <- paste(
+  "ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic
+  Antibodies)
+  Treatment-induced ADA = negative or missing baseline.
+  ADA result(s) and at least one positive post-baseline ADA result.
+  Transient ADA = ADA positive result detected (a) at only one post-baseline
+  sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints
+  during treatment where the first and last ADA positive samples are separated
+  by a period of < 16 weeks, irrespective of any negative samples in between.
+  Persistent ADA = ADA positive result detected (a) at the last post-baseline
+  sampling timepoint, OR (b) at 2 or more time points during treatment where
+  the first and last ADA positive samples are separated by a period ≥ 16
+  weeks, irrespective of any negative samples in between."
+)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| code-fold: show
+
+# In progress
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ADA/adat03.qmd b/book/tables/ADA/adat03.qmd
new file mode 100644
index 0000000000..561c5ab1af
--- /dev/null
+++ b/book/tables/ADA/adat03.qmd
@@ -0,0 +1,131 @@
+---
+title: ADAT03
+subtitle: Summary of Serum Concentrations at Timepoints Where ADA Samples Were Collected and Analyzed
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(dplyr)
+library(scda)
+library(tern)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adab <- synthetic_cdisc_dataset("latest", "adab")
+adpc <- synthetic_cdisc_dataset("latest", "adpc")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adab <- df_explicit_na(adab)
+adpc <- df_explicit_na(adpc)
+
+# Adjust zzz parameter
+max_conc <- 15
+
+adpc <- adpc %>% select(USUBJID, NFRLT, AVAL, AVALU, AVALCAT1)
+
+anl <- adab %>%
+  filter(., PARAM == "ADA interpreted per sample result") %>%
+  select(-AVAL, AVALC, AVALU)
+
+anl <- merge(anl, adpc, by = c("USUBJID", "NFRLT")) %>%
+  mutate(AVAL_LT = ifelse(AVAL <= max_conc, TRUE, FALSE))
+```
+
+## Standard Table (μg/mL)
+
+```{r variant1, test = list(result_v1 = "result")}
+# parameters in columns
+adat03_stats <- c("n", "mean", "sd", "median", "min", "max", "cv", "geom_mean", "count_fraction")
+adat03_lbls <- c(
+  n = "Total Number\nof Measurable\n Samples",
+  mean = "Mean",
+  sd = "SD",
+  median = "Median",
+  min = "Minimum",
+  max = "Maximum",
+  cv = "CV (%)",
+  geom_mean = "Geometric Mean",
+  count_fraction = paste0("Samples with\nConcentration\n≤ ", max_conc, "μg/mL")
+)
+adat03_fmts <- c(
+  n = "xx.",
+  mean = sprintf_format("%.3e"),
+  sd = sprintf_format("%.3e"),
+  median = sprintf_format("%.3e"),
+  min = sprintf_format("%.3e"),
+  max = sprintf_format("%.3e"),
+  cv = "xx.x",
+  geom_mean = sprintf_format("%.3e"),
+  count_fraction = format_count_fraction
+)
+
+afun_list <- lapply(
+  1:9,
+  function(i) make_afun(s_summary, .stats = adat03_stats[i], .formats = adat03_fmts[i], .labels = "Overall")
+)
+
+# lyt creation
+lyt <- basic_table() %>%
+  split_rows_by(
+    var = "ARM",
+    label_pos = "topleft",
+    split_label = "Treatment Group",
+    split_fun = drop_split_levels,
+    section_div = ""
+  ) %>%
+  add_rowcounts() %>%
+  split_rows_by(
+    var = "VISIT",
+    label_pos = "topleft",
+    split_label = "Visit",
+    split_fun = drop_split_levels,
+    child_labels = "hidden"
+  ) %>%
+  analyze_vars_in_cols(
+    vars = c(rep("AVAL", 8), "AVAL_LT"),
+    .stats = adat03_stats,
+    .labels = adat03_lbls,
+    .formats = adat03_fmts
+  ) %>%
+  analyze_colvars(
+    afun_list,
+    nested = FALSE,
+    extra_args = list(".labels" = "Overall")
+  )
+
+result <- build_table(lyt, anl, alt_counts_df = adsl)
+
+main_title(result) <- paste(
+  "Summary of Serum Concentrations (μg/mL) at Timepoints Where ADA Samples Were Collected and Analyzed\n
+  Protocol:", unique(adab$PARCAT1)[1]
+)
+subtitles(result) <- paste("Analyte:", unique(adab$PARAMCD)[1])
+fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL")) <- "Refers to the total no. of measurable ADA samples that have a corresponding measurable drug concentration sample (i.e. results with
+valid numeric values and LTRs). LTR results on post-dose samples are replaced by aaa µg/mL i.e. half of MQC value."
+fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL_LT")) <- "In validation, the assay was able to detect yyy ng/mL of surrogate ADA in the presence of zzz µg/mL of [drug]. BLQ = Below Limit of
+Quantitation, LTR = Lower than Reportable, MQC = Minimum Quantifiable Concentration, ADA = Anti-Drug Antibodies (is also referred to as ATA,
+or Anti-Therapeutic Antibodies). RXXXXXXX is also known as [drug]"
+
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| code-fold: show
+
+# In progress
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ADA/adat04a.qmd b/book/tables/ADA/adat04a.qmd
new file mode 100644
index 0000000000..3a3f56d998
--- /dev/null
+++ b/book/tables/ADA/adat04a.qmd
@@ -0,0 +1,240 @@
+---
+title: ADAT04A
+subtitle: Baseline Prevalence and Incidence of Treatment Emergent NAbs
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(tern)
+library(dplyr)
+library(scda)
+library(tibble)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adab <- synthetic_cdisc_dataset("latest", "adab")
+
+combodf <- tribble(
+  ~valname, ~label, ~levelcombo, ~exargs,
+  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
+  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
+)
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+
+# Baseline Pts
+adab_b <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL == "Y",
+    ADABLPFL == "Y",
+    PARAM %in% c(
+      "ADA interpreted per sample result",
+      "NAB interpreted per sample result"
+    )
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU) %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  mutate(
+    across(
+      any_of(c(
+        "ADA interpreted per sample result",
+        "NAB interpreted per sample result"
+      )),
+      as.logical
+    )
+  ) %>%
+  mutate(
+    ADABLPFL = ADABLPFL == "Y",
+    PADABLPFL = if (exists("ADA interpreted per sample result", where = .)) `ADA interpreted per sample result` == "TRUE" else FALSE,
+    PNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "TRUE" else FALSE,
+    NNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "FALSE" else FALSE
+  ) %>%
+  var_relabel(
+    ADABLPFL = "Baseline evaluable patients for ADA",
+    PADABLPFL = "Patients with a positive ADA sample at baseline",
+    PNABBLFL = "Patients with a positive NAb sample at baseline",
+    NNABBLFL = "Patient with no positive NAb samples at baseline"
+  )
+
+# Post Baseline Treatment Enhanced NAb positive Pts
+adab_pb <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL != "Y",
+    ADPBLPFL == "Y",
+    PARAM %in% c(
+      "ADA interpreted per sample result",
+      "Treatment Emergent - Positive, Neutralizing Antibody",
+      "Treatment induced ADA, Neutralizing Antibody",
+      "Treatment enhanced ADA, Neutralizing Antibody",
+      "NAB interpreted per sample result",
+      "Treatment unaffected, Neutralizing Antibody"
+    )
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %>%
+  unique() %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  mutate(
+    across(
+      any_of(c(
+        "ADA interpreted per sample result", "NAB interpreted per sample result",
+        "Treatment Emergent - Positive, Neutralizing Antibody",
+        "Treatment induced ADA, Neutralizing Antibody",
+        "Treatment enhanced ADA, Neutralizing Antibody",
+        "Treatment unaffected, Neutralizing Antibody"
+      )),
+      as.logical
+    )
+  ) %>%
+  mutate(
+    ADPBLPFL = ADPBLPFL == "Y",
+    ADAPFL = if (exists("ADA interpreted per sample result", where = .)) `ADA interpreted per sample result` == "TRUE" else FALSE,
+    TENABPFL = if (exists("Treatment Emergent - Positive, Neutralizing Antibody", where = .)) `Treatment Emergent - Positive, Neutralizing Antibody` == "TRUE" else FALSE,
+    TINPBFL = if (exists("Treatment induced ADA, Neutralizing Antibody", where = .)) `Treatment induced ADA, Neutralizing Antibody` == "TRUE" else FALSE,
+    TENPBFL = if (exists("Treatment enhanced ADA, Neutralizing Antibody", where = .)) `Treatment enhanced ADA, Neutralizing Antibody` == "TRUE" else FALSE,
+    NABNFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "FALSE" else FALSE,
+    TUNPBFL = if (exists("Treatment unaffected, Neutralizing Antibody", where = .)) `Treatment unaffected, Neutralizing Antibody` == "TRUE" else FALSE
+  ) %>%
+  var_relabel(
+    ADPBLPFL = "Post-baseline evaluable patients for ADA",
+    ADAPFL = "Patients positive for ADA",
+    TENABPFL = "Patients positive for Treatment Emergent NAb",
+    TINPBFL = "Treatment-induced NAb",
+    TENPBFL = "Treatment-enhanced NAb",
+    NABNFL = "Patients negative for NAb",
+    TUNPBFL = "Treatment unaffected"
+  )
+```
+
+## Summary of Treatment Emergent NAbs
+
+```{r variant1, test = list(result_v1 = "result")}
+# Layout for Baseline Prevalence of NAbs
+lyt_bl <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = add_combo_levels(combodf)
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("ADABLPFL", "PADABLPFL"),
+    table_names = "t1",
+    .stats = "count",
+    var_labels = "Baseline Prevalence of NAbs",
+    show_labels = "visible"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "PNABBLFL",
+    table_names = "t2",
+    .indent_mods = 1L
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NNABBLFL",
+    .stats = "count",
+    table_names = "t3",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  )
+
+# Layout for incidence of NAbs
+lyt_pb <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = add_combo_levels(combodf)
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("ADPBLPFL", "ADAPFL"),
+    .stats = "count",
+    var_labels = "Incidence of Treatment Emergent NAbs",
+    show_labels = "visible",
+    table_names = "tb1"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "TENABPFL",
+    table_names = "tb2",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("TINPBFL", "TENPBFL"),
+    .stats = "count",
+    table_names = "tb3",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NABNFL",
+    table_names = "tb4",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "TUNPBFL",
+    .stats = "count",
+    table_names = "tb5",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  )
+
+result_1 <- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)
+result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
+
+# Combine tables
+result <- rbind(result_1, result_2)
+
+# Change the column order
+result <- cbind_rtables(result[, 1], result[, 3]) %>%
+  cbind_rtables(result[, 4]) %>%
+  cbind_rtables(result[, 2]) %>%
+  cbind_rtables(result[, 5])
+
+main_title(result) <- paste(
+  "Baseline Prevalence and Incidence of Treatment Emergent NAbs"
+)
+subtitles(result) <- paste("Protocol:", unique(adab$PARCAT1)[1])
+main_footer(result) <- paste(
+  "NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s)
+  Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s)
+  Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample
+  Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample
+  Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.
+Number of patients positive for Treatment Emergent NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have treatment-induced NAb or treatment-enhanced NAb during the study period.
+Treatment-induced = a patient with negative or missing baseline result(s) and at least one positive post-baseline result. Treatment-enhanced = a patient with positive result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
+Number of patients negative for Treatment Emergent NAb = number of post-baseline evaluable patients with negative or missing baseline NAb result(s) and all negative post-baseline NAb results, or a patient who is NAb treatment unaffected.
+Treatment unaffected = A post-baseline evaluable patient with a positive result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing. For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer."
+)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| code-fold: show
+
+# In progress
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ADA/adat04b.qmd b/book/tables/ADA/adat04b.qmd
new file mode 100644
index 0000000000..5994f04002
--- /dev/null
+++ b/book/tables/ADA/adat04b.qmd
@@ -0,0 +1,223 @@
+---
+title: ADAT04B
+subtitle: Baseline Prevalence and Incidence of NAbs
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(tern)
+library(dplyr)
+library(scda)
+library(tibble)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adab <- synthetic_cdisc_dataset("latest", "adab")
+
+combodf <- tribble(
+  ~valname, ~label, ~levelcombo, ~exargs,
+  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
+  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
+)
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+
+# Baseline Pts
+adab_b <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL == "Y",
+    ADABLPFL == "Y",
+    PARAM %in% c(
+      "ADA interpreted per sample result",
+      "NAB interpreted per sample result"
+    )
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU) %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  mutate(
+    across(
+      any_of(c(
+        "ADA interpreted per sample result",
+        "NAB interpreted per sample result"
+      )),
+      as.logical
+    )
+  ) %>%
+  mutate(
+    ADABLPFL = ADABLPFL == "Y",
+    PADABLPFL = if (exists("ADA interpreted per sample result", where = .)) `ADA interpreted per sample result` == "TRUE" else FALSE,
+    PNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "TRUE" else FALSE,
+    NNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "FALSE" else FALSE
+  ) %>%
+  var_relabel(
+    ADABLPFL = "Baseline evaluable patients for ADA",
+    PADABLPFL = "Patients with a positive ADA sample at baseline",
+    PNABBLFL = "Patients with a positive NAb sample at baseline",
+    NNABBLFL = "Patients with no positive NAb sample at baseline"
+  )
+
+# Post-Baseline Evaluable Pts
+adab_pb_ada <- df_explicit_na(adab) %>%
+  filter(ADPBLPFL == "Y") %>%
+  select(STUDYID, USUBJID, ARM, ACTARM, ADPBLPFL) %>%
+  mutate(ADPBLPFL = ADPBLPFL == "Y") %>%
+  distinct()
+
+# Post-Baseline ADA Positive Pts
+adab_pb_adap <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL != "Y",
+    PARAM == "ADA interpreted per sample result",
+    AVALC == "POSITIVE"
+  ) %>%
+  mutate(ADAPFL = AVALC == "POSITIVE") %>%
+  select(STUDYID, USUBJID, ARM, ACTARM, ADAPFL) %>%
+  distinct()
+
+# Post-Baseline NAb Positive Pts
+adab_pb_nabp <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL != "Y",
+    PARAM == "NAb interpreted per sample result",
+    AVALC == "POSITIVE"
+  ) %>%
+  mutate(NABPFL = AVALC == "POSITIVE") %>%
+  select(STUDYID, USUBJID, ARM, ACTARM, NABPFL) %>%
+  distinct()
+
+# Post-Baseline NAb Negative Pts
+adab_pb_nabn <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL != "Y",
+    PARAM == "NAb interpreted per sample result",
+    AVALC == "NEGATIVE"
+  ) %>%
+  rename(NABNFL = AVALC) %>%
+  select(STUDYID, USUBJID, ARM, ACTARM, NABNFL) %>%
+  distinct()
+
+mergecol <- c("STUDYID", "USUBJID", "ARM", "ACTARM")
+
+adab_pb <- left_join(adab_pb_ada, adab_pb_adap, by = mergecol) %>%
+  left_join(adab_pb_nabp, by = mergecol) %>%
+  mutate(
+    NABNFL = ifelse(is.na(NABPFL), "TRUE", "FALSE"),
+    NABNFL = NABNFL == "TRUE"
+  ) %>%
+  var_relabel(
+    ADPBLPFL = "Post-baseline evaluable patients for ADA",
+    ADAPFL = "Patients positive for ADA",
+    NABPFL = "Patients positive for NAb",
+    NABNFL = "Patients negative for NAb"
+  )
+```
+
+## Standard Table
+
+```{r variant1, test = list(result_v1 = "result")}
+# Layout for Baseline Prevalence of NAbs
+lyt_bl <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = add_combo_levels(combodf)
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("ADABLPFL", "PADABLPFL"),
+    .stats = "count",
+    var_labels = "Baseline Prevalence of NAbs",
+    show_labels = "visible",
+    table_names = "t1"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "PNABBLFL",
+    table_names = "t2",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NNABBLFL",
+    .stats = "count",
+    table_names = "t3",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  )
+
+# Layout for incidence of NAbs
+lyt_pb <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = add_combo_levels(combodf)
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("ADPBLPFL", "ADAPFL"),
+    .stats = "count",
+    var_labels = "Incidence of NAbs",
+    show_labels = "visible",
+    table_names = "tb1"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NABPFL",
+    table_names = "tb2",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NABNFL",
+    .stats = "count",
+    table_names = "tb3",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  )
+
+result_1 <- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)
+result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
+
+# Combine tables.
+result <- rbind(result_1, result_2)
+
+# Change the column order.
+result <- cbind_rtables(result[, 1], result[, 3]) %>%
+  cbind_rtables(result[, 4]) %>%
+  cbind_rtables(result[, 2]) %>%
+  cbind_rtables(result[, 5])
+
+main_title(result) <- paste(
+  "Baseline Prevalence and Incidence of Neutralizing Antibodies (NAbs)"
+)
+subtitles(result) <- paste("Protocol:", unique(adab$PARCAT1)[1])
+main_footer(result) <- paste(
+  "NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s) Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s) Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.
+Number of patients positive for NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have at least one positive post-baseline NAb result during the study period. Number of patients negative for NAb = number of post-baseline evaluable patients with all negative post-baseline NAb results."
+)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| code-fold: show
+
+# In progress
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ECG/egt01.qmd b/book/tables/ECG/egt01.qmd
new file mode 100644
index 0000000000..017188e635
--- /dev/null
+++ b/book/tables/ECG/egt01.qmd
@@ -0,0 +1,142 @@
+---
+title: EGT01
+subtitle: ECG Results and Change from Baseline by Visit
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(dplyr)
+library(tern)
+library(scda)
+
+# Data should be filtered for the studied Parameter (`PARAM`) and the
+# Analysis Visit (`AVISIT`). According to the GDSR template, the values for
+# the `AVISIT` reported in the EGT01 standard may be:
+# 'POST-BASELINE MAXIMUM', 'POST-BASELINE MINIMUM', 'POST-BASELINE LAST'.
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adeg <- df_explicit_na(adeg)
+
+adeg_f <- adeg %>%
+  filter(ANL01FL == "Y") %>%
+  filter(PARAM %in% c("Heart Rate", "QT Duration", "RR Duration"))
+```
+
+## Standard Table
+
+```{r variant1, test = list(result_v1 = "result")}
+# Define the split function
+split_fun <- drop_split_levels
+
+afun <- function(x, .var, .spl_context, ...) {
+  n_fun <- sum(!is.na(x), na.rm = TRUE)
+  if (n_fun == 0) {
+    mean_sd_fun <- c(NA, NA)
+    median_fun <- NA
+    min_max_fun <- c(NA, NA)
+  } else {
+    mean_sd_fun <- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))
+    median_fun <- median(x, na.rm = TRUE)
+    min_max_fun <- c(min(x), max(x))
+  }
+  is_chg <- .var == "CHG"
+  is_baseline <- .spl_context$value[which(.spl_context$split == "AVISIT")] == "BASELINE"
+  if (is_baseline && is_chg) n_fun <- mean_sd_fun <- median_fun <- min_max_fun <- NULL
+
+  in_rows(
+    "n" = n_fun,
+    "Mean (SD)" = mean_sd_fun,
+    "Median" = median_fun,
+    "Min - Max" = min_max_fun,
+    .formats = list("n" = "xx", "Mean (SD)" = "xx.xx (xx.xx)", "Median" = "xx.xx", "Min - Max" = "xx.xx - xx.xx"),
+    .format_na_strs = list("n" = "NE", "Mean (SD)" = "NE (NE)", "Median" = "NE", "Min - Max" = "NE - NE")
+  )
+}
+
+lyt <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by("ACTARM") %>%
+  split_rows_by("PARAM", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f$PARAM)) %>%
+  split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f$AVISIT)) %>%
+  split_cols_by_multivar(
+    vars = c("AVAL", "CHG"),
+    varlabels = c("Value at Visit", "Change from\nBaseline")
+  ) %>%
+  analyze_colvars(afun = afun)
+
+result <- build_table(lyt, adeg_f, alt_counts_df = adsl)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| screenshot.opts = list(delay = 16)
+
+library(teal.modules.clinical)
+library(scda)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adeg <- df_explicit_na(adeg)
+
+app <- init(
+  data = cdisc_data(
+    cdisc_dataset("ADSL",
+      adsl,
+      code = 'ADSL <- df_explicit_na(synthetic_cdisc_dataset("latest", "adsl"))'
+    ),
+    cdisc_dataset("ADEG",
+      adeg,
+      code = 'ADEG <- df_explicit_na(synthetic_cdisc_dataset("latest", "adeg"))'
+    ),
+    check = TRUE
+  ),
+  modules = modules(
+    tm_t_summary_by(
+      label = "ECG Results and Change from Baseline by Visit",
+      dataname = "ADEG",
+      arm_var = choices_selected(
+        choices = variable_choices(adsl, c("ARM", "ARMCD")),
+        selected = "ARM"
+      ),
+      by_vars = choices_selected(
+        choices = variable_choices(adeg, c("PARAM", "AVISIT")),
+        selected = c("AVISIT")
+      ),
+      summarize_vars = choices_selected(
+        choices = variable_choices(adeg, c("AVAL", "CHG")),
+        selected = c("AVAL")
+      ),
+      useNA = "ifany",
+      paramcd = choices_selected(
+        choices = value_choices(adeg, "PARAMCD", "PARAM"),
+        selected = "HR"
+      ),
+      parallel_vars = TRUE
+    )
+  ),
+  filter = list(ADEG = list(AVAL = list()))
+)
+
+shinyApp(app$ui, app$server)
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ECG/egt02.qmd b/book/tables/ECG/egt02.qmd
new file mode 100644
index 0000000000..addbda506f
--- /dev/null
+++ b/book/tables/ECG/egt02.qmd
@@ -0,0 +1,126 @@
+---
+title: EGT02
+subtitle: ECG Abnormalities (EGT02_1 & EGT02_2)
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(dplyr)
+library(scda)
+library(tern)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adeg <- df_explicit_na(adeg)
+
+# Note:  We keep only post-baseline for analysis.
+adeg_f <- adeg %>%
+  filter(ONTRTFL == "Y") %>%
+  filter(PARAM %in% c("Heart Rate", "QT Duration", "RR Duration")) %>%
+  filter(ANRIND != "<Missing>") %>%
+  var_relabel(
+    PARAM = "Assessment",
+    ANRIND = "Abnormality"
+  )
+```
+
+## ECG Abnormalities Regardless <br/> of Abnormality at Baseline
+
+```{r variant1, test = list(result_v1 = "result")}
+split_fun <- drop_split_levels
+
+lyt <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(var = "ACTARM") %>%
+  split_rows_by(
+    "PARAM",
+    split_fun = split_fun,
+    label_pos = "topleft",
+    split_label = obj_label(adeg_f$PARAM)
+  ) %>%
+  count_abnormal("ANRIND", abnormal = list(Low = "LOW", High = "HIGH"), exclude_base_abn = FALSE) %>%
+  append_varlabels(adeg_f, "ANRIND", indent = 1L)
+
+result <- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl)
+result
+```
+
+## ECG Abnormalities Among Subjects <br/> Without Abnormality at Baseline
+
+```{r variant2, test = list(result_v2 = "result")}
+split_fun <- drop_split_levels
+
+lyt <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(var = "ACTARM") %>%
+  split_rows_by(
+    "PARAM",
+    split_fun = split_fun,
+    label_pos = "topleft",
+    split_label = obj_label(adeg_f$PARAM)
+  ) %>%
+  count_abnormal("ANRIND", abnormal = list(Low = "LOW", High = "HIGH"), exclude_base_abn = TRUE) %>%
+  append_varlabels(adeg_f, "ANRIND", indent = 1L)
+
+result <- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| screenshot.opts = list(delay = 18)
+
+library(scda)
+library(teal.modules.clinical)
+library(dplyr)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+app <- init(
+  data = cdisc_data(
+    cdisc_dataset("ADSL", adsl, code = 'ADSL <- synthetic_cdisc_dataset("latest", "adsl")'),
+    cdisc_dataset("ADEG", adeg, code = 'ADEG <- synthetic_cdisc_dataset("latest", "adeg")'),
+    check = TRUE
+  ),
+  modules = modules(
+    tm_t_abnormality(
+      label = "Abnormality Table",
+      dataname = "ADEG",
+      arm_var = choices_selected(
+        choices = variable_choices(adsl, subset = c("ARM", "ARMCD")),
+        selected = "ARM"
+      ),
+      by_vars = choices_selected(
+        choices = variable_choices(adeg, subset = c("EGCAT", "PARAM", "AVISIT")),
+        selected = c("PARAM"),
+        keep_order = TRUE
+      ),
+      grade = choices_selected(
+        choices = variable_choices(adeg, subset = "ANRIND"),
+        selected = "ANRIND",
+        fixed = TRUE
+      ),
+      abnormal = list(Low = "LOW", High = "HIGH"),
+      exclude_base_abn = FALSE
+    )
+  )
+)
+
+shinyApp(app$ui, app$server)
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ECG/egt03.qmd b/book/tables/ECG/egt03.qmd
new file mode 100644
index 0000000000..6d335eb12e
--- /dev/null
+++ b/book/tables/ECG/egt03.qmd
@@ -0,0 +1,204 @@
+---
+title: EGT03
+subtitle: Shift Table of ECG Interval Data -- Baseline Versus Minimum/Maximum Post-Baseline
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(scda)
+library(tern)
+library(dplyr)
+
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adeg <- df_explicit_na(adeg)
+
+adeg_labels <- var_labels(adeg)
+
+adeg_f_pbmin <- subset(
+  adeg,
+  PARAMCD == "HR" & # Heart Rate
+    SAFFL == "Y" & # "Safety Population Flag"
+    ONTRTFL == "Y" & # "On Treatment Record Flag"
+    AVISIT == "POST-BASELINE MINIMUM" # "Analysis Visit"
+)
+
+adeg_f_pbmax <- subset(
+  adeg,
+  PARAMCD == "HR" & # Heart Rate
+    SAFFL == "Y" & # "Safety Population Flag"
+    ONTRTFL == "Y" & # "On Treatment Record Flag"
+    AVISIT == "POST-BASELINE MAXIMUM" # "Analysis Visit"
+)
+
+var_labels(adeg_f_pbmin) <- adeg_labels
+var_labels(adeg_f_pbmax) <- adeg_labels
+```
+
+## Table of Baseline Versus <br/> Minimum Post-Baseline
+
+For the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N.
+For illustration purposes, missing data are added to the example dataset.
+
+```{r variant1, test = list(result_v1 = "result")}
+set.seed(123, kind = "Mersenne-Twister")
+
+adeg_f_pbmin$BNRIND <- factor(
+  adeg_f_pbmin$BNRIND,
+  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
+  labels = c("LOW", "NORMAL", "HIGH", "Missing")
+)
+adeg_f_pbmin$ANRIND <- factor(
+  adeg_f_pbmin$ANRIND,
+  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
+  labels = c("LOW", "NORMAL", "HIGH", "Missing")
+)
+
+adeg_f_pbmin$BNRIND[sample(1:nrow(adeg_f_pbmin), size = 5)] <- "Missing"
+adeg_f_pbmin$ANRIND[sample(1:nrow(adeg_f_pbmin), size = 5)] <- "Missing"
+
+attr(adeg_f_pbmin$ANRIND, "label") <- "Analysis Reference Range Indicator"
+attr(adeg_f_pbmin$BNRIND, "label") <- "Baseline Reference Range Indicator"
+
+# Temporary solution for overarching column
+adeg_f_pbmin <- adeg_f_pbmin %>% mutate(min_label = "Minimum Post-Baseline Assessment")
+
+# Define the split function
+split_fun <- drop_split_levels
+
+lyt <- basic_table() %>%
+  split_cols_by("min_label") %>%
+  split_cols_by("ANRIND") %>%
+  split_rows_by("ARMCD", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f_pbmin$ARMCD)) %>%
+  add_rowcounts() %>%
+  analyze_vars("BNRIND", denom = "N_row", .stats = "count_fraction") %>%
+  append_varlabels(adeg_f_pbmin, c("BNRIND"), indent = 1L)
+
+result <- build_table(
+  lyt = lyt,
+  df = adeg_f_pbmin
+)
+
+result
+```
+
+## Table of Baseline Versus <br/> Maximum Post-Baseline
+
+For the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N.
+For illustration purpose, missing data are added to the example dataset.
+
+```{r variant2, test = list(result_v2 = "result")}
+set.seed(123, kind = "Mersenne-Twister")
+
+adeg_f_pbmax$BNRIND <- factor(
+  adeg_f_pbmax$BNRIND,
+  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
+  labels = c("LOW", "NORMAL", "HIGH", "Missing")
+)
+adeg_f_pbmax$ANRIND <- factor(
+  adeg_f_pbmax$ANRIND,
+  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
+  labels = c("LOW", "NORMAL", "HIGH", "Missing")
+)
+
+adeg_f_pbmax$BNRIND[sample(1:nrow(adeg_f_pbmax), size = 5)] <- "Missing"
+adeg_f_pbmax$ANRIND[sample(1:nrow(adeg_f_pbmax), size = 5)] <- "Missing"
+
+attr(adeg_f_pbmax$ANRIND, "label") <- "Analysis Reference Range Indicator"
+attr(adeg_f_pbmax$BNRIND, "label") <- "Baseline Reference Range Indicator"
+
+# Temporary solution for overarching column
+adeg_f_pbmax <- adeg_f_pbmax %>% mutate(max_label = "Maximum Post-Baseline Assessment")
+
+# Define the split function
+split_fun <- drop_split_levels
+
+lyt <- basic_table() %>%
+  split_cols_by("max_label") %>%
+  split_cols_by("ANRIND") %>%
+  split_rows_by("ARMCD", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f_pbmax$ARMCD)) %>%
+  add_rowcounts() %>%
+  analyze_vars("BNRIND", denom = "N_row", .stats = "count_fraction") %>%
+  append_varlabels(adeg_f_pbmax, c("BNRIND"), indent = 1L)
+
+result <- build_table(
+  lyt = lyt,
+  df = adeg_f_pbmax
+)
+
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| screenshot.opts = list(delay = 16)
+
+library(dplyr)
+library(tern)
+library(scda)
+library(teal.modules.clinical)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+app <- init(
+  data = cdisc_data(
+    cdisc_dataset("ADSL", adsl, code = 'ADSL <- synthetic_cdisc_dataset("latest", "adsl")'),
+    cdisc_dataset("ADEG", adeg, code = 'ADEG <- synthetic_cdisc_dataset("latest", "adeg")'),
+    check = TRUE
+  ),
+  modules = modules(
+    tm_t_shift_by_arm(
+      label = "Shift by Arm Table",
+      dataname = "ADEG",
+      arm_var = choices_selected(
+        variable_choices(adsl, subset = c("ARM", "ARMCD")),
+        selected = "ARM"
+      ),
+      paramcd = choices_selected(
+        value_choices(adeg, "PARAMCD"),
+        selected = "HR"
+      ),
+      visit_var = choices_selected(
+        value_choices(adeg, "AVISIT"),
+        selected = "POST-BASELINE MINIMUM"
+      ),
+      aval_var = choices_selected(
+        variable_choices(adeg, subset = "ANRIND"),
+        selected = "ANRIND", fixed = TRUE
+      ),
+      base_var = choices_selected(
+        variable_choices(adeg, subset = "BNRIND"),
+        selected = "BNRIND", fixed = TRUE
+      ),
+      treatment_flag_var = choices_selected(
+        variable_choices(adeg, subset = "ONTRTFL"),
+        selected = "ONTRTFL", fixed = TRUE
+      ),
+      treatment_flag = choices_selected(
+        value_choices(adeg, "ONTRTFL"),
+        selected = "Y", fixed = TRUE
+      )
+    )
+  ),
+  filter = list(ADSL = list(SAFFL = "Y"))
+)
+
+shinyApp(app$ui, app$server)
+```
+
+{{< include ../../repro.qmd >}}
+
+:::
diff --git a/book/tables/ECG/egt04.qmd b/book/tables/ECG/egt04.qmd
new file mode 100644
index 0000000000..fb960b9b2a
--- /dev/null
+++ b/book/tables/ECG/egt04.qmd
@@ -0,0 +1,100 @@
+---
+title: EGT04
+subtitle: Shift Table of Qualitative ECG Assessments
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+For the EGT04 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N.
+For illustration purposes, missing data are added to the example dataset.
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(scda)
+library(tern)
+library(dplyr)
+set.seed(123)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adeg <- df_explicit_na(adeg, omit_columns = c("AVALC", "BASEC"))
+
+adeg_labels <- var_labels(adeg)
+
+adeg_f <- subset(
+  adeg,
+  PARAMCD == "ECGINTP" & # Analysis in terms of "NORMAL"/"ABNORMAL" (AVALC)
+    SAFFL == "Y" & # "Safety Population Flag"
+    ONTRTFL == "Y" & # "On Treatment Record Flag"
+    WORS02FL == "Y" # "Worst Post-Baseline Observation"
+)
+
+adeg_f$AVALC[sample(1:nrow(adeg_f), size = 5)] <- "Missing"
+adeg_f$BASEC[sample(1:nrow(adeg_f), size = 5)] <- "Missing"
+adeg_f$AVALC <- factor(
+  adeg_f$AVALC,
+  levels = c("NORMAL", "ABNORMAL", "Missing"),
+  labels = c("Normal", "Abnormal", "Missing")
+)
+adeg_f$BASEC <- factor(
+  adeg_f$BASEC,
+  levels = c("NORMAL", "ABNORMAL", "Missing"),
+  labels = c("Normal", "Abnormal", "Missing")
+)
+
+var_labels(adeg_f) <- adeg_labels
+adeg_f <- adeg_f %>%
+  var_relabel(BASEC = "Baseline")
+
+# Temprary solution for over arching column
+adeg_f <- adeg_f %>% mutate(postbaseline_label = "Post-Baseline")
+```
+
+## Standard Table
+
+The EGT04 template consists of a stacked list of contingency tables, one per group.
+For each group, the n's across all cells must add up to the group N in the analysis, and percentages are calculated using N as the denominator.
+
+```{r variant1, test = list(result_v1 = "result")}
+# Define the split function
+split_fun <- drop_split_levels
+
+lyt <- basic_table() %>%
+  split_cols_by("postbaseline_label") %>%
+  split_cols_by("AVALC") %>%
+  split_rows_by("ARM", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f$ARM)) %>%
+  add_rowcounts() %>%
+  analyze_vars(
+    "BASEC",
+    denom = "N_row",
+    .stats = "count_fraction",
+    na.rm = FALSE
+  ) %>%
+  append_varlabels(adeg_f, c("BASEC"), indent = 1L)
+
+result <- build_table(lyt, adeg_f)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| code-fold: show
+
+# In progress
+```
+
+{{< include ../../repro.qmd >}}
+
+:::
diff --git a/book/tables/ECG/egt05_qtcat.qmd b/book/tables/ECG/egt05_qtcat.qmd
new file mode 100644
index 0000000000..db249bacc5
--- /dev/null
+++ b/book/tables/ECG/egt05_qtcat.qmd
@@ -0,0 +1,255 @@
+---
+title: EGT05_QTCAT
+subtitle: ECG Actual Values and Changes from Baseline by Visit
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(tern)
+library(scda)
+library(dplyr)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adeg <- df_explicit_na(adeg)
+
+adeg_labels <- var_labels(adeg)
+adeg_f <- adeg %>%
+  filter(
+    PARAMCD == "QT",
+    ANL01FL == "Y"
+  ) %>%
+  mutate(
+    AVALCAT1 = case_when(
+      AVAL <= 450 ~ "<=450 msec",
+      AVAL <= 480 ~ ">450 to <=480 msec",
+      AVAL <= 500 ~ ">480 to <= 500 msec",
+      AVAL > 500 ~ ">500 msec",
+      is.na(AVAL) ~ "<Missing>"
+    ),
+    CHGCAT1 = case_when(
+      CHG <= 30 ~ "<=30 msec",
+      CHG <= 60 ~ ">30 to <=60 msec",
+      CHG > 60 ~ ">60 msec",
+      is.na(CHG) ~ "<Missing>"
+    )
+  ) %>%
+  mutate(
+    AVALCAT1 = factor(
+      AVALCAT1,
+      levels = c(
+        "<=450 msec",
+        ">450 to <=480 msec",
+        ">480 to <= 500 msec",
+        ">500 msec",
+        "<Missing>"
+      )
+    ),
+    CHGCAT1 = factor(
+      CHGCAT1,
+      levels = c(
+        "<=30 msec",
+        ">30 to <=60 msec",
+        ">60 msec",
+        "<Missing>"
+      )
+    )
+  ) %>%
+  var_relabel(
+    AVALCAT1 = "Value at Visit",
+    CHGCAT1 = "Change from Baseline"
+  )
+```
+
+## Standard Table
+
+```{r variant1, test = list(result_v1 = "result")}
+split_fun <- drop_split_levels
+
+lyt <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by("ARM") %>%
+  split_rows_by(
+    "PARAM",
+    split_label = obj_label(adeg_f$PARAM),
+    split_fun = split_fun,
+    label_pos = "topleft"
+  ) %>%
+  split_rows_by(
+    "AVISIT",
+    split_label = obj_label(adeg_f$AVISIT),
+    split_fun = split_fun,
+    label_pos = "topleft"
+  ) %>%
+  analyze_vars(
+    vars = c("AVALCAT1", "CHGCAT1"),
+    var_labels = c("Value at Visit", "Change from Baseline")
+  ) %>%
+  append_topleft("    Category")
+
+result <- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl) %>%
+  prune_table()
+
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| screenshot.opts = list(delay = 24)
+
+library(teal.modules.clinical)
+library(scda)
+library(dplyr)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+adsl <- df_explicit_na(adsl)
+adeg <- df_explicit_na(adeg)
+adeg_labels <- var_labels(adeg)
+
+adeg_f <- adeg %>%
+  filter(
+    ANL01FL == "Y" # no need to filter for PARAMCD here
+  ) %>%
+  mutate(
+    AVALCAT1 = case_when(
+      AVAL <= 450 ~ "<=450 msec",
+      AVAL <= 480 ~ ">450 to <=480 msec",
+      AVAL <= 500 ~ ">480 to <= 500 msec",
+      AVAL > 500 ~ ">500 msec",
+      is.na(AVAL) ~ "<Missing>"
+    ),
+    CHGCAT1 = case_when(
+      CHG <= 30 ~ "<=30 msec",
+      CHG <= 60 ~ ">30 to <=60 msec",
+      CHG > 60 ~ ">60 msec",
+      is.na(CHG) ~ "<Missing>"
+    )
+  ) %>%
+  mutate(
+    AVALCAT1 = factor(
+      AVALCAT1,
+      levels = c(
+        "<=450 msec",
+        ">450 to <=480 msec",
+        ">480 to <= 500 msec",
+        ">500 msec",
+        "<Missing>"
+      )
+    ),
+    CHGCAT1 = factor(
+      CHGCAT1,
+      levels = c(
+        "<=30 msec",
+        ">30 to <=60 msec",
+        ">60 msec",
+        "<Missing>"
+      )
+    )
+  )
+var_labels(adeg_f) <- c(
+  adeg_labels,
+  "AVALCAT1" = "Value at Visit",
+  "CHGCAT1" = "Change from Baseline"
+)
+app <- init(
+  data = cdisc_data(
+    cdisc_dataset("ADSL",
+      adsl,
+      code = 'ADSL <- synthetic_cdisc_dataset("latest", "adsl")
+                  ADSL <- df_explicit_na(ADSL)'
+    ),
+    cdisc_dataset("ADEG",
+      adeg_f,
+      code = 'ADEG <- synthetic_cdisc_dataset("latest", "adeg")
+                  ADEG <- df_explicit_na(ADEG)
+                  ADEG_labels <- var_labels(ADEG)
+
+                  ADEG <- ADEG %>%
+                    filter(
+                      ANL01FL == "Y"
+                    ) %>%
+                     mutate(
+                      AVALCAT1 = case_when(
+                        AVAL <= 450 ~ "<=450 msec",
+                        AVAL <= 480 ~ ">450 to <=480 msec",
+                        AVAL <= 500 ~ ">480 to <= 500 msec",
+                        AVAL > 500 ~ ">500 msec",
+                        is.na(AVAL) ~ "<Missing>"),
+                      CHGCAT1 = case_when(
+                        CHG <= 30 ~ "<=30 msec",
+                        CHG <= 60 ~ ">30 to <=60 msec",
+                        CHG > 60 ~ ">60 msec",
+                        is.na(CHG) ~ "<Missing>")
+                    ) %>% mutate(
+                      AVALCAT1 = factor(
+                        AVALCAT1,
+                        levels = c(
+                          "<=450 msec",
+                          ">450 to <=480 msec",
+                          ">480 to <= 500 msec",
+                          ">500 msec",
+                          "<Missing>"
+                        )
+                      ),
+                      CHGCAT1 = factor(
+                        CHGCAT1,
+                        levels = c(
+                          "<=30 msec",
+                          ">30 to <=60 msec",
+                          ">60 msec",
+                          "<Missing>"
+                        )
+                      )
+                    )
+                  var_labels(ADEG) <- c(
+                    ADEG_labels,
+                    "AVALCAT1" = "Value at Visit",
+                    "CHGCAT1" = "Change from Baseline"
+                  )'
+    ),
+    check = TRUE
+  ),
+  modules = modules(
+    tm_t_summary_by(
+      label = "ECG Actual Values and Changes from Baseline by Visit",
+      dataname = "ADEG",
+      arm_var = choices_selected(
+        choices = variable_choices(adsl, c("ARM", "ARMCD")),
+        selected = "ARM"
+      ),
+      by_vars = choices_selected(
+        choices = variable_choices(adeg_f, c("PARAM", "AVISIT")),
+        selected = c("AVISIT")
+      ),
+      summarize_vars = choices_selected(
+        choices = variable_choices(adeg_f, c("AVALCAT1", "CHGCAT1")),
+        selected = c("AVALCAT1", "CHGCAT1")
+      ),
+      useNA = "ifany",
+      paramcd = choices_selected(
+        choices = value_choices(adeg_f, "PARAMCD", "PARAM"),
+        selected = "QT"
+      )
+    )
+  )
+)
+
+shinyApp(app$ui, app$server)
+```
+
+{{< include ../../repro.qmd >}}
+:::

From ba2fd8fef439b93adb8b51cf74aab4431a59b241 Mon Sep 17 00:00:00 2001
From: Emily de la Rua <59304861+edelarua@users.noreply.github.com>
Date: Thu, 21 Sep 2023 12:48:16 -0400
Subject: [PATCH 05/11] Delete ada directory

Signed-off-by: Emily de la Rua <59304861+edelarua@users.noreply.github.com>
---
 book/tables/ada/adat01.qmd  | 230 ----------------------------------
 book/tables/ada/adat02.qmd  | 151 -----------------------
 book/tables/ada/adat03.qmd  | 131 --------------------
 book/tables/ada/adat04a.qmd | 240 ------------------------------------
 book/tables/ada/adat04b.qmd | 223 ---------------------------------
 5 files changed, 975 deletions(-)
 delete mode 100644 book/tables/ada/adat01.qmd
 delete mode 100644 book/tables/ada/adat02.qmd
 delete mode 100644 book/tables/ada/adat03.qmd
 delete mode 100644 book/tables/ada/adat04a.qmd
 delete mode 100644 book/tables/ada/adat04b.qmd

diff --git a/book/tables/ada/adat01.qmd b/book/tables/ada/adat01.qmd
deleted file mode 100644
index 31a13bd819..0000000000
--- a/book/tables/ada/adat01.qmd
+++ /dev/null
@@ -1,230 +0,0 @@
----
-title: ADAT01
-subtitle: Baseline Prevalence and Incidence of Treatment Emergent ADA
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(tern)
-library(dplyr)
-library(scda)
-library(tibble)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adab <- synthetic_cdisc_dataset("latest", "adab")
-
-combodf <- tribble(
-  ~valname, ~label, ~levelcombo, ~exargs,
-  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
-  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
-)
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adab <- adab %>%
-  filter(PARCAT1 == "A: Drug X Antibody") %>%
-  select(-ARRLT, -NRRLT)
-
-# Baseline Pts
-adab_b <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL == "Y",
-    ADABLPFL == "Y",
-    PARAM %in% c("ADA interpreted per sample result")
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU) %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  mutate_at(
-    c("ADA interpreted per sample result"),
-    as.logical
-  ) %>%
-  mutate(
-    ADABLPFL = ADABLPFL == "Y",
-    PADABLPFL = `ADA interpreted per sample result` == "TRUE",
-    NADABLPFL = `ADA interpreted per sample result` == "FALSE"
-  ) %>%
-  var_relabel(
-    ADABLPFL = "Baseline evaluable patients",
-    PADABLPFL = "Patient with a positive sample at baseline",
-    NADABLPFL = "Patient with no positive samples at baseline"
-  )
-
-# Post Baseline Treatment Enhanced NAb positive Pts
-adab_pb <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL != "Y",
-    ADPBLPFL == "Y",
-    PARAM %in% c(
-      "ADA interpreted per sample result",
-      "Treatment Emergent - Positive",
-      "Treatment induced ADA",
-      "Treatment enhanced ADA",
-      "Treatment Emergent - Negative",
-      "Treatment unaffected"
-    )
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU) %>%
-  unique() %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  mutate(
-    across(
-      any_of(c(
-        "ADA interpreted per sample result", "Treatment Emergent - Positive",
-        "Treatment induced ADA", "Treatment enhanced ADA",
-        "Treatment Emergent - Negative", "Treatment unaffected"
-      )),
-      as.logical
-    )
-  ) %>%
-  mutate(
-    ADPBLPFL = ADPBLPFL == "Y",
-    PTEFL = if (exists("Treatment Emergent - Positive", where = .)) `Treatment Emergent - Positive` == "TRUE" else FALSE,
-    TIFL = if (exists("Treatment induced ADA", where = .)) `Treatment induced ADA` == "TRUE" else FALSE,
-    TEFL = if (exists("Treatment enhanced ADA", where = .)) `Treatment enhanced ADA` == "TRUE" else FALSE,
-    NTEFL = if (exists("Treatment Emergent - Negative", where = .)) `Treatment Emergent - Negative` == "TRUE" else FALSE,
-    TUFL = if (exists("Treatment unaffected", where = .)) `Treatment unaffected` == "TRUE" else FALSE
-  ) %>%
-  var_relabel(
-    ADPBLPFL = "Post-baseline evaluable patients",
-    PTEFL = "Patient positive for Treatment Emergent ADA",
-    TIFL = "Treatment-induced ADA",
-    TEFL = "Treatment-enhanced ADA",
-    NTEFL = "Patient negative for Treatment Emergent ADA",
-    TUFL = "Treatment unaffected"
-  )
-```
-
-## Standard Table
-
-```{r variant1, test = list(result_v1 = "result")}
-# Layout for Baseline Prevalence of NAbs
-lyt_bl <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = add_combo_levels(combodf)
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "ADABLPFL",
-    .stats = "count",
-    var_labels = "Baseline Prevalence of ADAs",
-    show_labels = "visible",
-    table_names = "t1"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "PADABLPFL",
-    table_names = "t2",
-    .indent_mods = 1L,
-    var_labels = "a",
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NADABLPFL",
-    .stats = "count",
-    show_labels = "hidden",
-    .indent_mods = 1L,
-    table_names = "t3"
-  )
-
-# Layout for incidence of NAbs
-lyt_pb <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = add_combo_levels(combodf)
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "ADPBLPFL",
-    .stats = "count",
-    var_labels = "Incidence of Treatment Emergent ADAs",
-    show_labels = "visible",
-    table_names = "tb1"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "PTEFL",
-    table_names = "tb2",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("TIFL", "TEFL"),
-    .stats = "count",
-    table_names = "tb3",
-    .indent_mods = 2L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NTEFL",
-    table_names = "tb4",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "TUFL",
-    .stats = "count",
-    table_names = "tb5",
-    .indent_mods = 2L,
-    show_labels = "hidden"
-  )
-
-result_1 <- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)
-result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
-
-# Combine tables.
-result_1@col_info <- result_2@col_info
-result <- rbind(result_1, result_2)
-
-# Change the column order.
-result <- cbind_rtables(result[, 1], result[, 3]) %>%
-  cbind_rtables(result[, 4]) %>%
-  cbind_rtables(result[, 2]) %>%
-  cbind_rtables(result[, 5])
-
-main_title(result) <- paste(
-  "Baseline Prevalence and Incidence of Treatment Emergent ADA"
-)
-main_footer(result) <- paste(
-  "ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient = a patient with an ADA assay result from a baseline sample(s)
-  Post-baseline evaluable patient = a patient with an ADA assay result from at least one post-baseline sample Number of patients positive for Treatment Emergent
-  ADA = the number (and percentage) of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.
-  Treatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.
-  Treatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
-  Number of patients negative for Treatment Emergent ADA = number of post-baseline evaluable patients with negative or missing baseline ADA result(s) and all negative post-baseline results, or a patient who is treatment unaffected.
-  Treatment unaffected = A post-baseline evaluable patient with a positive ADA result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing.
-  For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer."
-)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| code-fold: show
-
-# In progress
-```
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ada/adat02.qmd b/book/tables/ada/adat02.qmd
deleted file mode 100644
index 630e8e817e..0000000000
--- a/book/tables/ada/adat02.qmd
+++ /dev/null
@@ -1,151 +0,0 @@
----
-title: ADAT02
-subtitle: Summary of Patients with Treatment-Induced ADA
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(tern)
-library(dplyr)
-library(scda)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adab <- synthetic_cdisc_dataset("latest", "adab")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adab <- df_explicit_na(adab) %>%
-  mutate(ADPBLPFL = "Y") %>% # temp fix
-  filter(
-    ADPBLPFL == "Y",
-    !PARAM %in% c(
-      "NAB interpreted per sample result",
-      "NAB Status of a patient",
-      "Treatment enhanced ADA"
-    )
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %>%
-  unique() %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  mutate(
-    across(
-      any_of(c(
-        "Treatment induced ADA",
-        "Transient ADA",
-        "Persistent ADA"
-      )),
-      as.logical
-    )
-  ) %>%
-  mutate(
-    ADPBLPFL = ADPBLPFL == "Y",
-    TI_ADA = if (exists("Treatment induced ADA", where = .)) `Treatment induced ADA` else FALSE
-  ) %>%
-  var_relabel(
-    ADPBLPFL = "Post-baseline evaluable patients",
-    TI_ADA = "Treatment-induced ADA patients"
-  )
-
-adab_ti <- adab %>%
-  filter(TI_ADA)
-```
-
-## Standard Table
-
-```{r variant1, test = list(result_v1 = "result")}
-# Layout for post-baseline evaluable patient variables from adab dataset.
-lyt_adab <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = drop_split_levels
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "ADPBLPFL",
-    .stats = "count",
-    table_names = "post_baseline"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "TI_ADA"
-  )
-
-# Layout for treatment-induced patient variables from adab dataset.
-lyt_adab_ti <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = keep_split_levels(c("A: Drug X", "C: Combination", "<Missing>")) # temp fix
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("Transient ADA", "Persistent ADA"),
-    var_labels = "Treatment-induced ADA patients with",
-    show_labels = "visible"
-  ) %>%
-  analyze_vars(
-    "Time to onset of ADA",
-    .stats = "median",
-    nested = FALSE,
-    .labels = c(median = "Median time to onset of ADA (weeks)")
-  ) %>%
-  analyze_vars(
-    "Antibody titer units",
-    .stats = "range",
-    nested = FALSE,
-    .labels = c(range = "ADA titer range (min - max)")
-  )
-
-result_adab <- build_table(lyt_adab, df = adab, alt_counts_df = adsl)
-result_adab_ti <- build_table(lyt_adab_ti, df = adab_ti, alt_counts_df = adsl)
-
-# Combine tables.
-col_info(result_adab) <- col_info(result_adab_ti)
-result <- rbind(
-  result_adab,
-  result_adab_ti
-)
-
-main_title(result) <- paste(
-  "Summary of Patients with Treatment-Induced ADA, PK Population"
-)
-subtitles(result) <- paste("Protocol:", unique(adab$PARCAT1)[1])
-main_footer(result) <- paste(
-  "ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic
-  Antibodies)
-  Treatment-induced ADA = negative or missing baseline.
-  ADA result(s) and at least one positive post-baseline ADA result.
-  Transient ADA = ADA positive result detected (a) at only one post-baseline
-  sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints
-  during treatment where the first and last ADA positive samples are separated
-  by a period of < 16 weeks, irrespective of any negative samples in between.
-  Persistent ADA = ADA positive result detected (a) at the last post-baseline
-  sampling timepoint, OR (b) at 2 or more time points during treatment where
-  the first and last ADA positive samples are separated by a period ≥ 16
-  weeks, irrespective of any negative samples in between."
-)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| code-fold: show
-
-# In progress
-```
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ada/adat03.qmd b/book/tables/ada/adat03.qmd
deleted file mode 100644
index 561c5ab1af..0000000000
--- a/book/tables/ada/adat03.qmd
+++ /dev/null
@@ -1,131 +0,0 @@
----
-title: ADAT03
-subtitle: Summary of Serum Concentrations at Timepoints Where ADA Samples Were Collected and Analyzed
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(dplyr)
-library(scda)
-library(tern)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adab <- synthetic_cdisc_dataset("latest", "adab")
-adpc <- synthetic_cdisc_dataset("latest", "adpc")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adab <- df_explicit_na(adab)
-adpc <- df_explicit_na(adpc)
-
-# Adjust zzz parameter
-max_conc <- 15
-
-adpc <- adpc %>% select(USUBJID, NFRLT, AVAL, AVALU, AVALCAT1)
-
-anl <- adab %>%
-  filter(., PARAM == "ADA interpreted per sample result") %>%
-  select(-AVAL, AVALC, AVALU)
-
-anl <- merge(anl, adpc, by = c("USUBJID", "NFRLT")) %>%
-  mutate(AVAL_LT = ifelse(AVAL <= max_conc, TRUE, FALSE))
-```
-
-## Standard Table (μg/mL)
-
-```{r variant1, test = list(result_v1 = "result")}
-# parameters in columns
-adat03_stats <- c("n", "mean", "sd", "median", "min", "max", "cv", "geom_mean", "count_fraction")
-adat03_lbls <- c(
-  n = "Total Number\nof Measurable\n Samples",
-  mean = "Mean",
-  sd = "SD",
-  median = "Median",
-  min = "Minimum",
-  max = "Maximum",
-  cv = "CV (%)",
-  geom_mean = "Geometric Mean",
-  count_fraction = paste0("Samples with\nConcentration\n≤ ", max_conc, "μg/mL")
-)
-adat03_fmts <- c(
-  n = "xx.",
-  mean = sprintf_format("%.3e"),
-  sd = sprintf_format("%.3e"),
-  median = sprintf_format("%.3e"),
-  min = sprintf_format("%.3e"),
-  max = sprintf_format("%.3e"),
-  cv = "xx.x",
-  geom_mean = sprintf_format("%.3e"),
-  count_fraction = format_count_fraction
-)
-
-afun_list <- lapply(
-  1:9,
-  function(i) make_afun(s_summary, .stats = adat03_stats[i], .formats = adat03_fmts[i], .labels = "Overall")
-)
-
-# lyt creation
-lyt <- basic_table() %>%
-  split_rows_by(
-    var = "ARM",
-    label_pos = "topleft",
-    split_label = "Treatment Group",
-    split_fun = drop_split_levels,
-    section_div = ""
-  ) %>%
-  add_rowcounts() %>%
-  split_rows_by(
-    var = "VISIT",
-    label_pos = "topleft",
-    split_label = "Visit",
-    split_fun = drop_split_levels,
-    child_labels = "hidden"
-  ) %>%
-  analyze_vars_in_cols(
-    vars = c(rep("AVAL", 8), "AVAL_LT"),
-    .stats = adat03_stats,
-    .labels = adat03_lbls,
-    .formats = adat03_fmts
-  ) %>%
-  analyze_colvars(
-    afun_list,
-    nested = FALSE,
-    extra_args = list(".labels" = "Overall")
-  )
-
-result <- build_table(lyt, anl, alt_counts_df = adsl)
-
-main_title(result) <- paste(
-  "Summary of Serum Concentrations (μg/mL) at Timepoints Where ADA Samples Were Collected and Analyzed\n
-  Protocol:", unique(adab$PARCAT1)[1]
-)
-subtitles(result) <- paste("Analyte:", unique(adab$PARAMCD)[1])
-fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL")) <- "Refers to the total no. of measurable ADA samples that have a corresponding measurable drug concentration sample (i.e. results with
-valid numeric values and LTRs). LTR results on post-dose samples are replaced by aaa µg/mL i.e. half of MQC value."
-fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL_LT")) <- "In validation, the assay was able to detect yyy ng/mL of surrogate ADA in the presence of zzz µg/mL of [drug]. BLQ = Below Limit of
-Quantitation, LTR = Lower than Reportable, MQC = Minimum Quantifiable Concentration, ADA = Anti-Drug Antibodies (is also referred to as ATA,
-or Anti-Therapeutic Antibodies). RXXXXXXX is also known as [drug]"
-
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| code-fold: show
-
-# In progress
-```
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ada/adat04a.qmd b/book/tables/ada/adat04a.qmd
deleted file mode 100644
index 3a3f56d998..0000000000
--- a/book/tables/ada/adat04a.qmd
+++ /dev/null
@@ -1,240 +0,0 @@
----
-title: ADAT04A
-subtitle: Baseline Prevalence and Incidence of Treatment Emergent NAbs
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(tern)
-library(dplyr)
-library(scda)
-library(tibble)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adab <- synthetic_cdisc_dataset("latest", "adab")
-
-combodf <- tribble(
-  ~valname, ~label, ~levelcombo, ~exargs,
-  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
-  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
-)
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-
-# Baseline Pts
-adab_b <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL == "Y",
-    ADABLPFL == "Y",
-    PARAM %in% c(
-      "ADA interpreted per sample result",
-      "NAB interpreted per sample result"
-    )
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU) %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  mutate(
-    across(
-      any_of(c(
-        "ADA interpreted per sample result",
-        "NAB interpreted per sample result"
-      )),
-      as.logical
-    )
-  ) %>%
-  mutate(
-    ADABLPFL = ADABLPFL == "Y",
-    PADABLPFL = if (exists("ADA interpreted per sample result", where = .)) `ADA interpreted per sample result` == "TRUE" else FALSE,
-    PNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "TRUE" else FALSE,
-    NNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "FALSE" else FALSE
-  ) %>%
-  var_relabel(
-    ADABLPFL = "Baseline evaluable patients for ADA",
-    PADABLPFL = "Patients with a positive ADA sample at baseline",
-    PNABBLFL = "Patients with a positive NAb sample at baseline",
-    NNABBLFL = "Patient with no positive NAb samples at baseline"
-  )
-
-# Post Baseline Treatment Enhanced NAb positive Pts
-adab_pb <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL != "Y",
-    ADPBLPFL == "Y",
-    PARAM %in% c(
-      "ADA interpreted per sample result",
-      "Treatment Emergent - Positive, Neutralizing Antibody",
-      "Treatment induced ADA, Neutralizing Antibody",
-      "Treatment enhanced ADA, Neutralizing Antibody",
-      "NAB interpreted per sample result",
-      "Treatment unaffected, Neutralizing Antibody"
-    )
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %>%
-  unique() %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  mutate(
-    across(
-      any_of(c(
-        "ADA interpreted per sample result", "NAB interpreted per sample result",
-        "Treatment Emergent - Positive, Neutralizing Antibody",
-        "Treatment induced ADA, Neutralizing Antibody",
-        "Treatment enhanced ADA, Neutralizing Antibody",
-        "Treatment unaffected, Neutralizing Antibody"
-      )),
-      as.logical
-    )
-  ) %>%
-  mutate(
-    ADPBLPFL = ADPBLPFL == "Y",
-    ADAPFL = if (exists("ADA interpreted per sample result", where = .)) `ADA interpreted per sample result` == "TRUE" else FALSE,
-    TENABPFL = if (exists("Treatment Emergent - Positive, Neutralizing Antibody", where = .)) `Treatment Emergent - Positive, Neutralizing Antibody` == "TRUE" else FALSE,
-    TINPBFL = if (exists("Treatment induced ADA, Neutralizing Antibody", where = .)) `Treatment induced ADA, Neutralizing Antibody` == "TRUE" else FALSE,
-    TENPBFL = if (exists("Treatment enhanced ADA, Neutralizing Antibody", where = .)) `Treatment enhanced ADA, Neutralizing Antibody` == "TRUE" else FALSE,
-    NABNFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "FALSE" else FALSE,
-    TUNPBFL = if (exists("Treatment unaffected, Neutralizing Antibody", where = .)) `Treatment unaffected, Neutralizing Antibody` == "TRUE" else FALSE
-  ) %>%
-  var_relabel(
-    ADPBLPFL = "Post-baseline evaluable patients for ADA",
-    ADAPFL = "Patients positive for ADA",
-    TENABPFL = "Patients positive for Treatment Emergent NAb",
-    TINPBFL = "Treatment-induced NAb",
-    TENPBFL = "Treatment-enhanced NAb",
-    NABNFL = "Patients negative for NAb",
-    TUNPBFL = "Treatment unaffected"
-  )
-```
-
-## Summary of Treatment Emergent NAbs
-
-```{r variant1, test = list(result_v1 = "result")}
-# Layout for Baseline Prevalence of NAbs
-lyt_bl <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = add_combo_levels(combodf)
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("ADABLPFL", "PADABLPFL"),
-    table_names = "t1",
-    .stats = "count",
-    var_labels = "Baseline Prevalence of NAbs",
-    show_labels = "visible"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "PNABBLFL",
-    table_names = "t2",
-    .indent_mods = 1L
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NNABBLFL",
-    .stats = "count",
-    table_names = "t3",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  )
-
-# Layout for incidence of NAbs
-lyt_pb <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = add_combo_levels(combodf)
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("ADPBLPFL", "ADAPFL"),
-    .stats = "count",
-    var_labels = "Incidence of Treatment Emergent NAbs",
-    show_labels = "visible",
-    table_names = "tb1"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "TENABPFL",
-    table_names = "tb2",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("TINPBFL", "TENPBFL"),
-    .stats = "count",
-    table_names = "tb3",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NABNFL",
-    table_names = "tb4",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "TUNPBFL",
-    .stats = "count",
-    table_names = "tb5",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  )
-
-result_1 <- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)
-result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
-
-# Combine tables
-result <- rbind(result_1, result_2)
-
-# Change the column order
-result <- cbind_rtables(result[, 1], result[, 3]) %>%
-  cbind_rtables(result[, 4]) %>%
-  cbind_rtables(result[, 2]) %>%
-  cbind_rtables(result[, 5])
-
-main_title(result) <- paste(
-  "Baseline Prevalence and Incidence of Treatment Emergent NAbs"
-)
-subtitles(result) <- paste("Protocol:", unique(adab$PARCAT1)[1])
-main_footer(result) <- paste(
-  "NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s)
-  Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s)
-  Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample
-  Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample
-  Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.
-Number of patients positive for Treatment Emergent NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have treatment-induced NAb or treatment-enhanced NAb during the study period.
-Treatment-induced = a patient with negative or missing baseline result(s) and at least one positive post-baseline result. Treatment-enhanced = a patient with positive result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
-Number of patients negative for Treatment Emergent NAb = number of post-baseline evaluable patients with negative or missing baseline NAb result(s) and all negative post-baseline NAb results, or a patient who is NAb treatment unaffected.
-Treatment unaffected = A post-baseline evaluable patient with a positive result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing. For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer."
-)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| code-fold: show
-
-# In progress
-```
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ada/adat04b.qmd b/book/tables/ada/adat04b.qmd
deleted file mode 100644
index 5994f04002..0000000000
--- a/book/tables/ada/adat04b.qmd
+++ /dev/null
@@ -1,223 +0,0 @@
----
-title: ADAT04B
-subtitle: Baseline Prevalence and Incidence of NAbs
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(tern)
-library(dplyr)
-library(scda)
-library(tibble)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adab <- synthetic_cdisc_dataset("latest", "adab")
-
-combodf <- tribble(
-  ~valname, ~label, ~levelcombo, ~exargs,
-  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
-  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
-)
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-
-# Baseline Pts
-adab_b <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL == "Y",
-    ADABLPFL == "Y",
-    PARAM %in% c(
-      "ADA interpreted per sample result",
-      "NAB interpreted per sample result"
-    )
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU) %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  mutate(
-    across(
-      any_of(c(
-        "ADA interpreted per sample result",
-        "NAB interpreted per sample result"
-      )),
-      as.logical
-    )
-  ) %>%
-  mutate(
-    ADABLPFL = ADABLPFL == "Y",
-    PADABLPFL = if (exists("ADA interpreted per sample result", where = .)) `ADA interpreted per sample result` == "TRUE" else FALSE,
-    PNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "TRUE" else FALSE,
-    NNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "FALSE" else FALSE
-  ) %>%
-  var_relabel(
-    ADABLPFL = "Baseline evaluable patients for ADA",
-    PADABLPFL = "Patients with a positive ADA sample at baseline",
-    PNABBLFL = "Patients with a positive NAb sample at baseline",
-    NNABBLFL = "Patients with no positive NAb sample at baseline"
-  )
-
-# Post-Baseline Evaluable Pts
-adab_pb_ada <- df_explicit_na(adab) %>%
-  filter(ADPBLPFL == "Y") %>%
-  select(STUDYID, USUBJID, ARM, ACTARM, ADPBLPFL) %>%
-  mutate(ADPBLPFL = ADPBLPFL == "Y") %>%
-  distinct()
-
-# Post-Baseline ADA Positive Pts
-adab_pb_adap <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL != "Y",
-    PARAM == "ADA interpreted per sample result",
-    AVALC == "POSITIVE"
-  ) %>%
-  mutate(ADAPFL = AVALC == "POSITIVE") %>%
-  select(STUDYID, USUBJID, ARM, ACTARM, ADAPFL) %>%
-  distinct()
-
-# Post-Baseline NAb Positive Pts
-adab_pb_nabp <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL != "Y",
-    PARAM == "NAb interpreted per sample result",
-    AVALC == "POSITIVE"
-  ) %>%
-  mutate(NABPFL = AVALC == "POSITIVE") %>%
-  select(STUDYID, USUBJID, ARM, ACTARM, NABPFL) %>%
-  distinct()
-
-# Post-Baseline NAb Negative Pts
-adab_pb_nabn <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL != "Y",
-    PARAM == "NAb interpreted per sample result",
-    AVALC == "NEGATIVE"
-  ) %>%
-  rename(NABNFL = AVALC) %>%
-  select(STUDYID, USUBJID, ARM, ACTARM, NABNFL) %>%
-  distinct()
-
-mergecol <- c("STUDYID", "USUBJID", "ARM", "ACTARM")
-
-adab_pb <- left_join(adab_pb_ada, adab_pb_adap, by = mergecol) %>%
-  left_join(adab_pb_nabp, by = mergecol) %>%
-  mutate(
-    NABNFL = ifelse(is.na(NABPFL), "TRUE", "FALSE"),
-    NABNFL = NABNFL == "TRUE"
-  ) %>%
-  var_relabel(
-    ADPBLPFL = "Post-baseline evaluable patients for ADA",
-    ADAPFL = "Patients positive for ADA",
-    NABPFL = "Patients positive for NAb",
-    NABNFL = "Patients negative for NAb"
-  )
-```
-
-## Standard Table
-
-```{r variant1, test = list(result_v1 = "result")}
-# Layout for Baseline Prevalence of NAbs
-lyt_bl <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = add_combo_levels(combodf)
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("ADABLPFL", "PADABLPFL"),
-    .stats = "count",
-    var_labels = "Baseline Prevalence of NAbs",
-    show_labels = "visible",
-    table_names = "t1"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "PNABBLFL",
-    table_names = "t2",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NNABBLFL",
-    .stats = "count",
-    table_names = "t3",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  )
-
-# Layout for incidence of NAbs
-lyt_pb <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = add_combo_levels(combodf)
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("ADPBLPFL", "ADAPFL"),
-    .stats = "count",
-    var_labels = "Incidence of NAbs",
-    show_labels = "visible",
-    table_names = "tb1"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NABPFL",
-    table_names = "tb2",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NABNFL",
-    .stats = "count",
-    table_names = "tb3",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  )
-
-result_1 <- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)
-result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
-
-# Combine tables.
-result <- rbind(result_1, result_2)
-
-# Change the column order.
-result <- cbind_rtables(result[, 1], result[, 3]) %>%
-  cbind_rtables(result[, 4]) %>%
-  cbind_rtables(result[, 2]) %>%
-  cbind_rtables(result[, 5])
-
-main_title(result) <- paste(
-  "Baseline Prevalence and Incidence of Neutralizing Antibodies (NAbs)"
-)
-subtitles(result) <- paste("Protocol:", unique(adab$PARCAT1)[1])
-main_footer(result) <- paste(
-  "NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s) Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s) Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.
-Number of patients positive for NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have at least one positive post-baseline NAb result during the study period. Number of patients negative for NAb = number of post-baseline evaluable patients with all negative post-baseline NAb results."
-)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| code-fold: show
-
-# In progress
-```
-
-{{< include ../../repro.qmd >}}
-:::

From eb1abcf553faa0dead5b0876f52925b747c73ed9 Mon Sep 17 00:00:00 2001
From: Emily de la Rua <59304861+edelarua@users.noreply.github.com>
Date: Thu, 21 Sep 2023 12:48:53 -0400
Subject: [PATCH 06/11] Delete tables/ecg directory

Signed-off-by: Emily de la Rua <59304861+edelarua@users.noreply.github.com>
---
 book/tables/ecg/egt01.qmd       | 142 ------------------
 book/tables/ecg/egt02.qmd       | 126 ----------------
 book/tables/ecg/egt03.qmd       | 204 -------------------------
 book/tables/ecg/egt04.qmd       | 100 -------------
 book/tables/ecg/egt05_qtcat.qmd | 255 --------------------------------
 5 files changed, 827 deletions(-)
 delete mode 100644 book/tables/ecg/egt01.qmd
 delete mode 100644 book/tables/ecg/egt02.qmd
 delete mode 100644 book/tables/ecg/egt03.qmd
 delete mode 100644 book/tables/ecg/egt04.qmd
 delete mode 100644 book/tables/ecg/egt05_qtcat.qmd

diff --git a/book/tables/ecg/egt01.qmd b/book/tables/ecg/egt01.qmd
deleted file mode 100644
index 017188e635..0000000000
--- a/book/tables/ecg/egt01.qmd
+++ /dev/null
@@ -1,142 +0,0 @@
----
-title: EGT01
-subtitle: ECG Results and Change from Baseline by Visit
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(dplyr)
-library(tern)
-library(scda)
-
-# Data should be filtered for the studied Parameter (`PARAM`) and the
-# Analysis Visit (`AVISIT`). According to the GDSR template, the values for
-# the `AVISIT` reported in the EGT01 standard may be:
-# 'POST-BASELINE MAXIMUM', 'POST-BASELINE MINIMUM', 'POST-BASELINE LAST'.
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adeg <- df_explicit_na(adeg)
-
-adeg_f <- adeg %>%
-  filter(ANL01FL == "Y") %>%
-  filter(PARAM %in% c("Heart Rate", "QT Duration", "RR Duration"))
-```
-
-## Standard Table
-
-```{r variant1, test = list(result_v1 = "result")}
-# Define the split function
-split_fun <- drop_split_levels
-
-afun <- function(x, .var, .spl_context, ...) {
-  n_fun <- sum(!is.na(x), na.rm = TRUE)
-  if (n_fun == 0) {
-    mean_sd_fun <- c(NA, NA)
-    median_fun <- NA
-    min_max_fun <- c(NA, NA)
-  } else {
-    mean_sd_fun <- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))
-    median_fun <- median(x, na.rm = TRUE)
-    min_max_fun <- c(min(x), max(x))
-  }
-  is_chg <- .var == "CHG"
-  is_baseline <- .spl_context$value[which(.spl_context$split == "AVISIT")] == "BASELINE"
-  if (is_baseline && is_chg) n_fun <- mean_sd_fun <- median_fun <- min_max_fun <- NULL
-
-  in_rows(
-    "n" = n_fun,
-    "Mean (SD)" = mean_sd_fun,
-    "Median" = median_fun,
-    "Min - Max" = min_max_fun,
-    .formats = list("n" = "xx", "Mean (SD)" = "xx.xx (xx.xx)", "Median" = "xx.xx", "Min - Max" = "xx.xx - xx.xx"),
-    .format_na_strs = list("n" = "NE", "Mean (SD)" = "NE (NE)", "Median" = "NE", "Min - Max" = "NE - NE")
-  )
-}
-
-lyt <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by("ACTARM") %>%
-  split_rows_by("PARAM", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f$PARAM)) %>%
-  split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f$AVISIT)) %>%
-  split_cols_by_multivar(
-    vars = c("AVAL", "CHG"),
-    varlabels = c("Value at Visit", "Change from\nBaseline")
-  ) %>%
-  analyze_colvars(afun = afun)
-
-result <- build_table(lyt, adeg_f, alt_counts_df = adsl)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| screenshot.opts = list(delay = 16)
-
-library(teal.modules.clinical)
-library(scda)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adeg <- df_explicit_na(adeg)
-
-app <- init(
-  data = cdisc_data(
-    cdisc_dataset("ADSL",
-      adsl,
-      code = 'ADSL <- df_explicit_na(synthetic_cdisc_dataset("latest", "adsl"))'
-    ),
-    cdisc_dataset("ADEG",
-      adeg,
-      code = 'ADEG <- df_explicit_na(synthetic_cdisc_dataset("latest", "adeg"))'
-    ),
-    check = TRUE
-  ),
-  modules = modules(
-    tm_t_summary_by(
-      label = "ECG Results and Change from Baseline by Visit",
-      dataname = "ADEG",
-      arm_var = choices_selected(
-        choices = variable_choices(adsl, c("ARM", "ARMCD")),
-        selected = "ARM"
-      ),
-      by_vars = choices_selected(
-        choices = variable_choices(adeg, c("PARAM", "AVISIT")),
-        selected = c("AVISIT")
-      ),
-      summarize_vars = choices_selected(
-        choices = variable_choices(adeg, c("AVAL", "CHG")),
-        selected = c("AVAL")
-      ),
-      useNA = "ifany",
-      paramcd = choices_selected(
-        choices = value_choices(adeg, "PARAMCD", "PARAM"),
-        selected = "HR"
-      ),
-      parallel_vars = TRUE
-    )
-  ),
-  filter = list(ADEG = list(AVAL = list()))
-)
-
-shinyApp(app$ui, app$server)
-```
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ecg/egt02.qmd b/book/tables/ecg/egt02.qmd
deleted file mode 100644
index addbda506f..0000000000
--- a/book/tables/ecg/egt02.qmd
+++ /dev/null
@@ -1,126 +0,0 @@
----
-title: EGT02
-subtitle: ECG Abnormalities (EGT02_1 & EGT02_2)
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(dplyr)
-library(scda)
-library(tern)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adeg <- df_explicit_na(adeg)
-
-# Note:  We keep only post-baseline for analysis.
-adeg_f <- adeg %>%
-  filter(ONTRTFL == "Y") %>%
-  filter(PARAM %in% c("Heart Rate", "QT Duration", "RR Duration")) %>%
-  filter(ANRIND != "<Missing>") %>%
-  var_relabel(
-    PARAM = "Assessment",
-    ANRIND = "Abnormality"
-  )
-```
-
-## ECG Abnormalities Regardless <br/> of Abnormality at Baseline
-
-```{r variant1, test = list(result_v1 = "result")}
-split_fun <- drop_split_levels
-
-lyt <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(var = "ACTARM") %>%
-  split_rows_by(
-    "PARAM",
-    split_fun = split_fun,
-    label_pos = "topleft",
-    split_label = obj_label(adeg_f$PARAM)
-  ) %>%
-  count_abnormal("ANRIND", abnormal = list(Low = "LOW", High = "HIGH"), exclude_base_abn = FALSE) %>%
-  append_varlabels(adeg_f, "ANRIND", indent = 1L)
-
-result <- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl)
-result
-```
-
-## ECG Abnormalities Among Subjects <br/> Without Abnormality at Baseline
-
-```{r variant2, test = list(result_v2 = "result")}
-split_fun <- drop_split_levels
-
-lyt <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(var = "ACTARM") %>%
-  split_rows_by(
-    "PARAM",
-    split_fun = split_fun,
-    label_pos = "topleft",
-    split_label = obj_label(adeg_f$PARAM)
-  ) %>%
-  count_abnormal("ANRIND", abnormal = list(Low = "LOW", High = "HIGH"), exclude_base_abn = TRUE) %>%
-  append_varlabels(adeg_f, "ANRIND", indent = 1L)
-
-result <- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| screenshot.opts = list(delay = 18)
-
-library(scda)
-library(teal.modules.clinical)
-library(dplyr)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-app <- init(
-  data = cdisc_data(
-    cdisc_dataset("ADSL", adsl, code = 'ADSL <- synthetic_cdisc_dataset("latest", "adsl")'),
-    cdisc_dataset("ADEG", adeg, code = 'ADEG <- synthetic_cdisc_dataset("latest", "adeg")'),
-    check = TRUE
-  ),
-  modules = modules(
-    tm_t_abnormality(
-      label = "Abnormality Table",
-      dataname = "ADEG",
-      arm_var = choices_selected(
-        choices = variable_choices(adsl, subset = c("ARM", "ARMCD")),
-        selected = "ARM"
-      ),
-      by_vars = choices_selected(
-        choices = variable_choices(adeg, subset = c("EGCAT", "PARAM", "AVISIT")),
-        selected = c("PARAM"),
-        keep_order = TRUE
-      ),
-      grade = choices_selected(
-        choices = variable_choices(adeg, subset = "ANRIND"),
-        selected = "ANRIND",
-        fixed = TRUE
-      ),
-      abnormal = list(Low = "LOW", High = "HIGH"),
-      exclude_base_abn = FALSE
-    )
-  )
-)
-
-shinyApp(app$ui, app$server)
-```
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ecg/egt03.qmd b/book/tables/ecg/egt03.qmd
deleted file mode 100644
index 6d335eb12e..0000000000
--- a/book/tables/ecg/egt03.qmd
+++ /dev/null
@@ -1,204 +0,0 @@
----
-title: EGT03
-subtitle: Shift Table of ECG Interval Data -- Baseline Versus Minimum/Maximum Post-Baseline
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(scda)
-library(tern)
-library(dplyr)
-
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adeg <- df_explicit_na(adeg)
-
-adeg_labels <- var_labels(adeg)
-
-adeg_f_pbmin <- subset(
-  adeg,
-  PARAMCD == "HR" & # Heart Rate
-    SAFFL == "Y" & # "Safety Population Flag"
-    ONTRTFL == "Y" & # "On Treatment Record Flag"
-    AVISIT == "POST-BASELINE MINIMUM" # "Analysis Visit"
-)
-
-adeg_f_pbmax <- subset(
-  adeg,
-  PARAMCD == "HR" & # Heart Rate
-    SAFFL == "Y" & # "Safety Population Flag"
-    ONTRTFL == "Y" & # "On Treatment Record Flag"
-    AVISIT == "POST-BASELINE MAXIMUM" # "Analysis Visit"
-)
-
-var_labels(adeg_f_pbmin) <- adeg_labels
-var_labels(adeg_f_pbmax) <- adeg_labels
-```
-
-## Table of Baseline Versus <br/> Minimum Post-Baseline
-
-For the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N.
-For illustration purposes, missing data are added to the example dataset.
-
-```{r variant1, test = list(result_v1 = "result")}
-set.seed(123, kind = "Mersenne-Twister")
-
-adeg_f_pbmin$BNRIND <- factor(
-  adeg_f_pbmin$BNRIND,
-  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
-  labels = c("LOW", "NORMAL", "HIGH", "Missing")
-)
-adeg_f_pbmin$ANRIND <- factor(
-  adeg_f_pbmin$ANRIND,
-  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
-  labels = c("LOW", "NORMAL", "HIGH", "Missing")
-)
-
-adeg_f_pbmin$BNRIND[sample(1:nrow(adeg_f_pbmin), size = 5)] <- "Missing"
-adeg_f_pbmin$ANRIND[sample(1:nrow(adeg_f_pbmin), size = 5)] <- "Missing"
-
-attr(adeg_f_pbmin$ANRIND, "label") <- "Analysis Reference Range Indicator"
-attr(adeg_f_pbmin$BNRIND, "label") <- "Baseline Reference Range Indicator"
-
-# Temporary solution for overarching column
-adeg_f_pbmin <- adeg_f_pbmin %>% mutate(min_label = "Minimum Post-Baseline Assessment")
-
-# Define the split function
-split_fun <- drop_split_levels
-
-lyt <- basic_table() %>%
-  split_cols_by("min_label") %>%
-  split_cols_by("ANRIND") %>%
-  split_rows_by("ARMCD", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f_pbmin$ARMCD)) %>%
-  add_rowcounts() %>%
-  analyze_vars("BNRIND", denom = "N_row", .stats = "count_fraction") %>%
-  append_varlabels(adeg_f_pbmin, c("BNRIND"), indent = 1L)
-
-result <- build_table(
-  lyt = lyt,
-  df = adeg_f_pbmin
-)
-
-result
-```
-
-## Table of Baseline Versus <br/> Maximum Post-Baseline
-
-For the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N.
-For illustration purpose, missing data are added to the example dataset.
-
-```{r variant2, test = list(result_v2 = "result")}
-set.seed(123, kind = "Mersenne-Twister")
-
-adeg_f_pbmax$BNRIND <- factor(
-  adeg_f_pbmax$BNRIND,
-  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
-  labels = c("LOW", "NORMAL", "HIGH", "Missing")
-)
-adeg_f_pbmax$ANRIND <- factor(
-  adeg_f_pbmax$ANRIND,
-  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
-  labels = c("LOW", "NORMAL", "HIGH", "Missing")
-)
-
-adeg_f_pbmax$BNRIND[sample(1:nrow(adeg_f_pbmax), size = 5)] <- "Missing"
-adeg_f_pbmax$ANRIND[sample(1:nrow(adeg_f_pbmax), size = 5)] <- "Missing"
-
-attr(adeg_f_pbmax$ANRIND, "label") <- "Analysis Reference Range Indicator"
-attr(adeg_f_pbmax$BNRIND, "label") <- "Baseline Reference Range Indicator"
-
-# Temporary solution for overarching column
-adeg_f_pbmax <- adeg_f_pbmax %>% mutate(max_label = "Maximum Post-Baseline Assessment")
-
-# Define the split function
-split_fun <- drop_split_levels
-
-lyt <- basic_table() %>%
-  split_cols_by("max_label") %>%
-  split_cols_by("ANRIND") %>%
-  split_rows_by("ARMCD", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f_pbmax$ARMCD)) %>%
-  add_rowcounts() %>%
-  analyze_vars("BNRIND", denom = "N_row", .stats = "count_fraction") %>%
-  append_varlabels(adeg_f_pbmax, c("BNRIND"), indent = 1L)
-
-result <- build_table(
-  lyt = lyt,
-  df = adeg_f_pbmax
-)
-
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| screenshot.opts = list(delay = 16)
-
-library(dplyr)
-library(tern)
-library(scda)
-library(teal.modules.clinical)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-app <- init(
-  data = cdisc_data(
-    cdisc_dataset("ADSL", adsl, code = 'ADSL <- synthetic_cdisc_dataset("latest", "adsl")'),
-    cdisc_dataset("ADEG", adeg, code = 'ADEG <- synthetic_cdisc_dataset("latest", "adeg")'),
-    check = TRUE
-  ),
-  modules = modules(
-    tm_t_shift_by_arm(
-      label = "Shift by Arm Table",
-      dataname = "ADEG",
-      arm_var = choices_selected(
-        variable_choices(adsl, subset = c("ARM", "ARMCD")),
-        selected = "ARM"
-      ),
-      paramcd = choices_selected(
-        value_choices(adeg, "PARAMCD"),
-        selected = "HR"
-      ),
-      visit_var = choices_selected(
-        value_choices(adeg, "AVISIT"),
-        selected = "POST-BASELINE MINIMUM"
-      ),
-      aval_var = choices_selected(
-        variable_choices(adeg, subset = "ANRIND"),
-        selected = "ANRIND", fixed = TRUE
-      ),
-      base_var = choices_selected(
-        variable_choices(adeg, subset = "BNRIND"),
-        selected = "BNRIND", fixed = TRUE
-      ),
-      treatment_flag_var = choices_selected(
-        variable_choices(adeg, subset = "ONTRTFL"),
-        selected = "ONTRTFL", fixed = TRUE
-      ),
-      treatment_flag = choices_selected(
-        value_choices(adeg, "ONTRTFL"),
-        selected = "Y", fixed = TRUE
-      )
-    )
-  ),
-  filter = list(ADSL = list(SAFFL = "Y"))
-)
-
-shinyApp(app$ui, app$server)
-```
-
-{{< include ../../repro.qmd >}}
-
-:::
diff --git a/book/tables/ecg/egt04.qmd b/book/tables/ecg/egt04.qmd
deleted file mode 100644
index fb960b9b2a..0000000000
--- a/book/tables/ecg/egt04.qmd
+++ /dev/null
@@ -1,100 +0,0 @@
----
-title: EGT04
-subtitle: Shift Table of Qualitative ECG Assessments
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-For the EGT04 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N.
-For illustration purposes, missing data are added to the example dataset.
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(scda)
-library(tern)
-library(dplyr)
-set.seed(123)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adeg <- df_explicit_na(adeg, omit_columns = c("AVALC", "BASEC"))
-
-adeg_labels <- var_labels(adeg)
-
-adeg_f <- subset(
-  adeg,
-  PARAMCD == "ECGINTP" & # Analysis in terms of "NORMAL"/"ABNORMAL" (AVALC)
-    SAFFL == "Y" & # "Safety Population Flag"
-    ONTRTFL == "Y" & # "On Treatment Record Flag"
-    WORS02FL == "Y" # "Worst Post-Baseline Observation"
-)
-
-adeg_f$AVALC[sample(1:nrow(adeg_f), size = 5)] <- "Missing"
-adeg_f$BASEC[sample(1:nrow(adeg_f), size = 5)] <- "Missing"
-adeg_f$AVALC <- factor(
-  adeg_f$AVALC,
-  levels = c("NORMAL", "ABNORMAL", "Missing"),
-  labels = c("Normal", "Abnormal", "Missing")
-)
-adeg_f$BASEC <- factor(
-  adeg_f$BASEC,
-  levels = c("NORMAL", "ABNORMAL", "Missing"),
-  labels = c("Normal", "Abnormal", "Missing")
-)
-
-var_labels(adeg_f) <- adeg_labels
-adeg_f <- adeg_f %>%
-  var_relabel(BASEC = "Baseline")
-
-# Temprary solution for over arching column
-adeg_f <- adeg_f %>% mutate(postbaseline_label = "Post-Baseline")
-```
-
-## Standard Table
-
-The EGT04 template consists of a stacked list of contingency tables, one per group.
-For each group, the n's across all cells must add up to the group N in the analysis, and percentages are calculated using N as the denominator.
-
-```{r variant1, test = list(result_v1 = "result")}
-# Define the split function
-split_fun <- drop_split_levels
-
-lyt <- basic_table() %>%
-  split_cols_by("postbaseline_label") %>%
-  split_cols_by("AVALC") %>%
-  split_rows_by("ARM", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f$ARM)) %>%
-  add_rowcounts() %>%
-  analyze_vars(
-    "BASEC",
-    denom = "N_row",
-    .stats = "count_fraction",
-    na.rm = FALSE
-  ) %>%
-  append_varlabels(adeg_f, c("BASEC"), indent = 1L)
-
-result <- build_table(lyt, adeg_f)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| code-fold: show
-
-# In progress
-```
-
-{{< include ../../repro.qmd >}}
-
-:::
diff --git a/book/tables/ecg/egt05_qtcat.qmd b/book/tables/ecg/egt05_qtcat.qmd
deleted file mode 100644
index db249bacc5..0000000000
--- a/book/tables/ecg/egt05_qtcat.qmd
+++ /dev/null
@@ -1,255 +0,0 @@
----
-title: EGT05_QTCAT
-subtitle: ECG Actual Values and Changes from Baseline by Visit
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(tern)
-library(scda)
-library(dplyr)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adeg <- df_explicit_na(adeg)
-
-adeg_labels <- var_labels(adeg)
-adeg_f <- adeg %>%
-  filter(
-    PARAMCD == "QT",
-    ANL01FL == "Y"
-  ) %>%
-  mutate(
-    AVALCAT1 = case_when(
-      AVAL <= 450 ~ "<=450 msec",
-      AVAL <= 480 ~ ">450 to <=480 msec",
-      AVAL <= 500 ~ ">480 to <= 500 msec",
-      AVAL > 500 ~ ">500 msec",
-      is.na(AVAL) ~ "<Missing>"
-    ),
-    CHGCAT1 = case_when(
-      CHG <= 30 ~ "<=30 msec",
-      CHG <= 60 ~ ">30 to <=60 msec",
-      CHG > 60 ~ ">60 msec",
-      is.na(CHG) ~ "<Missing>"
-    )
-  ) %>%
-  mutate(
-    AVALCAT1 = factor(
-      AVALCAT1,
-      levels = c(
-        "<=450 msec",
-        ">450 to <=480 msec",
-        ">480 to <= 500 msec",
-        ">500 msec",
-        "<Missing>"
-      )
-    ),
-    CHGCAT1 = factor(
-      CHGCAT1,
-      levels = c(
-        "<=30 msec",
-        ">30 to <=60 msec",
-        ">60 msec",
-        "<Missing>"
-      )
-    )
-  ) %>%
-  var_relabel(
-    AVALCAT1 = "Value at Visit",
-    CHGCAT1 = "Change from Baseline"
-  )
-```
-
-## Standard Table
-
-```{r variant1, test = list(result_v1 = "result")}
-split_fun <- drop_split_levels
-
-lyt <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by("ARM") %>%
-  split_rows_by(
-    "PARAM",
-    split_label = obj_label(adeg_f$PARAM),
-    split_fun = split_fun,
-    label_pos = "topleft"
-  ) %>%
-  split_rows_by(
-    "AVISIT",
-    split_label = obj_label(adeg_f$AVISIT),
-    split_fun = split_fun,
-    label_pos = "topleft"
-  ) %>%
-  analyze_vars(
-    vars = c("AVALCAT1", "CHGCAT1"),
-    var_labels = c("Value at Visit", "Change from Baseline")
-  ) %>%
-  append_topleft("    Category")
-
-result <- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl) %>%
-  prune_table()
-
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| screenshot.opts = list(delay = 24)
-
-library(teal.modules.clinical)
-library(scda)
-library(dplyr)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-adsl <- df_explicit_na(adsl)
-adeg <- df_explicit_na(adeg)
-adeg_labels <- var_labels(adeg)
-
-adeg_f <- adeg %>%
-  filter(
-    ANL01FL == "Y" # no need to filter for PARAMCD here
-  ) %>%
-  mutate(
-    AVALCAT1 = case_when(
-      AVAL <= 450 ~ "<=450 msec",
-      AVAL <= 480 ~ ">450 to <=480 msec",
-      AVAL <= 500 ~ ">480 to <= 500 msec",
-      AVAL > 500 ~ ">500 msec",
-      is.na(AVAL) ~ "<Missing>"
-    ),
-    CHGCAT1 = case_when(
-      CHG <= 30 ~ "<=30 msec",
-      CHG <= 60 ~ ">30 to <=60 msec",
-      CHG > 60 ~ ">60 msec",
-      is.na(CHG) ~ "<Missing>"
-    )
-  ) %>%
-  mutate(
-    AVALCAT1 = factor(
-      AVALCAT1,
-      levels = c(
-        "<=450 msec",
-        ">450 to <=480 msec",
-        ">480 to <= 500 msec",
-        ">500 msec",
-        "<Missing>"
-      )
-    ),
-    CHGCAT1 = factor(
-      CHGCAT1,
-      levels = c(
-        "<=30 msec",
-        ">30 to <=60 msec",
-        ">60 msec",
-        "<Missing>"
-      )
-    )
-  )
-var_labels(adeg_f) <- c(
-  adeg_labels,
-  "AVALCAT1" = "Value at Visit",
-  "CHGCAT1" = "Change from Baseline"
-)
-app <- init(
-  data = cdisc_data(
-    cdisc_dataset("ADSL",
-      adsl,
-      code = 'ADSL <- synthetic_cdisc_dataset("latest", "adsl")
-                  ADSL <- df_explicit_na(ADSL)'
-    ),
-    cdisc_dataset("ADEG",
-      adeg_f,
-      code = 'ADEG <- synthetic_cdisc_dataset("latest", "adeg")
-                  ADEG <- df_explicit_na(ADEG)
-                  ADEG_labels <- var_labels(ADEG)
-
-                  ADEG <- ADEG %>%
-                    filter(
-                      ANL01FL == "Y"
-                    ) %>%
-                     mutate(
-                      AVALCAT1 = case_when(
-                        AVAL <= 450 ~ "<=450 msec",
-                        AVAL <= 480 ~ ">450 to <=480 msec",
-                        AVAL <= 500 ~ ">480 to <= 500 msec",
-                        AVAL > 500 ~ ">500 msec",
-                        is.na(AVAL) ~ "<Missing>"),
-                      CHGCAT1 = case_when(
-                        CHG <= 30 ~ "<=30 msec",
-                        CHG <= 60 ~ ">30 to <=60 msec",
-                        CHG > 60 ~ ">60 msec",
-                        is.na(CHG) ~ "<Missing>")
-                    ) %>% mutate(
-                      AVALCAT1 = factor(
-                        AVALCAT1,
-                        levels = c(
-                          "<=450 msec",
-                          ">450 to <=480 msec",
-                          ">480 to <= 500 msec",
-                          ">500 msec",
-                          "<Missing>"
-                        )
-                      ),
-                      CHGCAT1 = factor(
-                        CHGCAT1,
-                        levels = c(
-                          "<=30 msec",
-                          ">30 to <=60 msec",
-                          ">60 msec",
-                          "<Missing>"
-                        )
-                      )
-                    )
-                  var_labels(ADEG) <- c(
-                    ADEG_labels,
-                    "AVALCAT1" = "Value at Visit",
-                    "CHGCAT1" = "Change from Baseline"
-                  )'
-    ),
-    check = TRUE
-  ),
-  modules = modules(
-    tm_t_summary_by(
-      label = "ECG Actual Values and Changes from Baseline by Visit",
-      dataname = "ADEG",
-      arm_var = choices_selected(
-        choices = variable_choices(adsl, c("ARM", "ARMCD")),
-        selected = "ARM"
-      ),
-      by_vars = choices_selected(
-        choices = variable_choices(adeg_f, c("PARAM", "AVISIT")),
-        selected = c("AVISIT")
-      ),
-      summarize_vars = choices_selected(
-        choices = variable_choices(adeg_f, c("AVALCAT1", "CHGCAT1")),
-        selected = c("AVALCAT1", "CHGCAT1")
-      ),
-      useNA = "ifany",
-      paramcd = choices_selected(
-        choices = value_choices(adeg_f, "PARAMCD", "PARAM"),
-        selected = "QT"
-      )
-    )
-  )
-)
-
-shinyApp(app$ui, app$server)
-```
-
-{{< include ../../repro.qmd >}}
-:::

From b0cb19eeca91c202024cb4a1f9d876e20a457ab1 Mon Sep 17 00:00:00 2001
From: Emily de la Rua <59304861+edelarua@users.noreply.github.com>
Date: Thu, 21 Sep 2023 12:49:10 -0400
Subject: [PATCH 07/11] Delete listings/ada directory

Signed-off-by: Emily de la Rua <59304861+edelarua@users.noreply.github.com>
---
 book/listings/ada/adal02.qmd | 131 -----------------------------------
 1 file changed, 131 deletions(-)
 delete mode 100644 book/listings/ada/adal02.qmd

diff --git a/book/listings/ada/adal02.qmd b/book/listings/ada/adal02.qmd
deleted file mode 100644
index 9ddcaf7f56..0000000000
--- a/book/listings/ada/adal02.qmd
+++ /dev/null
@@ -1,131 +0,0 @@
----
-title: ADAL02
-subtitle: Listing of Anti-Drug Antibody Data for Treatment Emergent ADA Positive Patients
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(dplyr)
-library(rlistings)
-library(scda)
-
-adab <- synthetic_cdisc_dataset("latest", "adab") %>%
-  filter(NFRLT %% 1 == 0 & NFRLT > 0)
-
-trt <- "A: Drug X"
-drug_a <- "A: Drug X Antibody"
-drugcd <- unique(adab$PARAMCD[adab$PARAM == "Antibody titer units"])[1]
-min_titer <- 1.10
-
-adab_x <- adab %>%
-  filter(
-    ARM == trt,
-    PARCAT1 == drug_a,
-    ADPBLPFL == "Y"
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %>%
-  unique() %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  filter(if_any(matches("Treatment Emergent - Positive"), ~ .x == 1)) %>%
-  # filter(`Treatment Emergent - Positive` == 1) %>%
-  mutate(
-    VISN = factor(paste0(
-      VISIT, "\n(Day ",
-      ifelse(
-        NFRLT %% 1 == 0,
-        NFRLT,
-        as.character(format(round(NFRLT, 2), nsmall = 2))
-      ),
-      ")"
-    )),
-    PTES = ifelse(
-      ifelse("Treatment induced ADA" %in% names(.), `Treatment induced ADA` == 1, F),
-      ifelse(
-        "Transient ADA" %in% names(.) & `Transient ADA` == 1,
-        "Induced (Transient)",
-        "Induced (Persistent)"
-      ),
-      "Enhanced"
-    )
-  ) %>%
-  mutate(
-    AVAL = paste0(
-      ifelse(
-        ifelse("ADA interpreted per sample result" %in% names(.), `ADA interpreted per sample result` == 0, F),
-        "NEGATIVE",
-        ifelse(
-          ifelse("Antibody titer units" %in% names(.), !is.na(`Antibody titer units`), F),
-          ifelse(
-            `Antibody titer units` < min_titer,
-            paste0("<", format(min_titer, nsmall = 2)),
-            as.character(format(round(`Antibody titer units`, 2), nsmall = 2))
-          ),
-          "---"
-        )
-      ),
-      ifelse(
-        ifelse("NAB interpreted per sample result" %in% names(.), `NAB interpreted per sample result` == 1, F),
-        "*",
-        ""
-      )
-    )
-  )
-
-out <- adab_x %>%
-  select(USUBJID, VISN, AVAL, PTES) %>%
-  tidyr::pivot_wider(
-    names_from = VISN,
-    values_from = AVAL
-  ) %>%
-  select(USUBJID, unique(adab_x$VISN[order(adab_x$NFRLT)]), PTES)
-
-var_labels(out) <- names(out)
-
-out <- out %>%
-  var_relabel(
-    USUBJID = "Subject ID",
-    PTES = "Patient Treatment\nEmergent ADA Status"
-  )
-```
-
-## Standard Listing
-
-```{r lsting, test = list(lsting = "lsting")}
-lsting <- as_listing(
-  out,
-  key_cols = "USUBJID",
-  disp_cols = names(out),
-  main_title = paste0(
-    "Listing of Anti-", drugcd, " Antibody Data for Treatment Emergent ADA Positive Patients, PK Population",
-    "\nProtocol: ", unique(adab$PARCAT1)[1]
-  ),
-  subtitles = paste("\nTreatment Group:", trt),
-  main_footer = "Minimum reportable titer = 1.10 (example only)
---- = No sample evaluated
-ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies)
-Number of patients positive for Treatment Emergent ADA = the number of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.
-Treatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.
-Treatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
-Transient ADA = ADA positive result detected (a) at only one post-baseline sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints during treatment where the first and last ADA positive samples  are separated by a period of < 16 weeks, irrespective of any negative samples in between.
-Persistent ADA =  ADA positive result detected (a) at the last post-baseline sampling timepoint, OR (b) at 2 or more time points during treatment where the first and last ADA positive samples are separated by a period ≥ 16 weeks, irrespective of any negative samples in between.
-Asterisk denotes sample that tested positive for Neutralizing Antibodies."
-)
-
-head(lsting, 20)
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-{{< include ../../repro.qmd >}}
-:::

From 33e21cc982145ef85597a9fa21b8031d20374440 Mon Sep 17 00:00:00 2001
From: Emily de la Rua <59304861+edelarua@users.noreply.github.com>
Date: Thu, 21 Sep 2023 12:49:30 -0400
Subject: [PATCH 08/11] Delete listings/ecg directory

Signed-off-by: Emily de la Rua <59304861+edelarua@users.noreply.github.com>
---
 book/listings/ecg/egl01.qmd | 89 -------------------------------------
 1 file changed, 89 deletions(-)
 delete mode 100644 book/listings/ecg/egl01.qmd

diff --git a/book/listings/ecg/egl01.qmd b/book/listings/ecg/egl01.qmd
deleted file mode 100644
index a595b864c9..0000000000
--- a/book/listings/ecg/egl01.qmd
+++ /dev/null
@@ -1,89 +0,0 @@
----
-title: EGL01
-subtitle: 'Listing of ECG Data: Safety-Evaluable Patients'
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(dplyr)
-library(scda)
-library(rlistings)
-
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-get_param_unit_range <- function(dataset) {
-  u_rng <- lapply(unique(dataset$PARAMCD), function(p) {
-    dat <- dataset %>% filter(PARAMCD == p)
-    list(
-      unit = unique(dat$AVALU),
-      range = paste0(unique(dat$ANRLO), "-", unique(dat$ANRHI))
-    )
-  })
-  names(u_rng) <- unique(dataset$PARAMCD)
-  u_rng
-}
-
-eg_u_rng <- get_param_unit_range(adeg)
-
-adeg_sub <- adeg %>%
-  filter(!is.na(AVAL) & SAFFL == "Y" & ANL01FL == "Y" & !is.na(EGSEQ) & PARAMCD != "ECGINTP") %>%
-  mutate(
-    CRTNPT = paste(SITEID, sub("^.*-([[:alnum:]]+)$", "\\1", SUBJID), sep = "/"),
-    AGSXRC = paste(AGE, SEX, RACE, sep = "/"),
-    AVAL = format(round(AVAL, 2), nsmall = 2),
-    AVAL_ANRIND = ifelse(ANRIND %in% c("NORMAL", ""), AVAL, paste(AVAL, substr(ANRIND, 1, 1), sep = "/")),
-    CHG = format(round(CHG, 2), nsmall = 2)
-  )
-
-anl_eg <- adeg_sub %>%
-  select(SUBJID, CRTNPT, AGSXRC, TRT01A, PARAMCD, AVAL_ANRIND, CHG, ADY, AVISIT, ADTM) %>%
-  tidyr::pivot_wider(
-    id_cols = c(SUBJID, CRTNPT, AGSXRC, TRT01A, ADY, AVISIT, ADTM),
-    names_from = PARAMCD,
-    values_from = c(AVAL_ANRIND, CHG)
-  )
-
-out <- anl_eg %>%
-  select(CRTNPT, AGSXRC, TRT01A, AVISIT, ADY, AVAL_ANRIND_HR, CHG_HR, AVAL_ANRIND_QT, CHG_QT, AVAL_ANRIND_RR, CHG_RR) %>%
-  var_relabel(
-    CRTNPT = "Center/Subject ID",
-    AGSXRC = "Age/Sex/Race",
-    TRT01A = "Treatment",
-    AVISIT = "Visit",
-    ADY = "Study\nDay",
-    AVAL_ANRIND_HR = paste0("Heart Rate Result\n(", eg_u_rng$HR$unit, ");\nRange:(", eg_u_rng$HR$range, ")"),
-    CHG_HR = "Heart Rate\nChange from BL",
-    AVAL_ANRIND_QT = paste0("QT Duration Result\n(", eg_u_rng$QT$unit, ");\nRange:(", eg_u_rng$QT$range, ")"),
-    CHG_QT = "QT Duration\nChange from BL",
-    AVAL_ANRIND_RR = paste0("RR Duration Result\n(", eg_u_rng$RR$unit, ");\nRange:(", eg_u_rng$RR$range, ")"),
-    CHG_RR = "RR Duration\nChange from BL"
-  )
-```
-
-## Standard Listing
-
-```{r lsting, test = list(lsting = "lsting")}
-lsting <- as_listing(
-  out,
-  key_cols = c("CRTNPT", "AGSXRC", "TRT01A", "AVISIT", "ADY"),
-  disp_cols = names(out),
-  main_title = "Listing of ECG Data: Safety-Evaluable Patients",
-  main_footer = "Baseline is the patient's last observation prior to initiation of study drug. Abnormalities are flagged as high (H) or low (L) if
-  outside the Roche standard reference range."
-)
-
-head(lsting, 20)
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-{{< include ../../repro.qmd >}}
-:::

From 0dc258605e83069e1457fe2d0a68f9980a1d5de7 Mon Sep 17 00:00:00 2001
From: Abinaya Yogasekaram <73252787+ayogasekaram@users.noreply.github.com>
Date: Thu, 21 Sep 2023 13:01:05 -0400
Subject: [PATCH 09/11] Apply suggestions from code review

adding code review suggestions

Co-authored-by: Emily de la Rua <59304861+edelarua@users.noreply.github.com>
Signed-off-by: Abinaya Yogasekaram <73252787+ayogasekaram@users.noreply.github.com>
---
 book/generate-index.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/book/generate-index.R b/book/generate-index.R
index 99cec8b787..91176eeda3 100755
--- a/book/generate-index.R
+++ b/book/generate-index.R
@@ -37,7 +37,7 @@ cat(
 # Tables
 
 section_header("Tables")
-create_subsection("./tables/ada", "ADA")
+create_subsection("./tables/ADA", "ADA")
 create_subsection("./tables/adverse-events", "Adverse Events")
 create_subsection("./tables/concomitant-medications", "Concomitant Medications")
 create_subsection("./tables/deaths", "Deaths")
@@ -59,9 +59,9 @@ create_subsection("./tables/vital-signs", "Vital Signs")
 section_header("Listings")
 create_subsection("./listings/ada", "ADA")
 create_subsection("./listings/adverse-events", "Adverse Events")
-create_subsection("./listings/concomitant-medications", "Concomitant Medications")
+create_subsection("./listings/concomitant-medications", "Concomitant Medications") # nolint
 create_subsection("./listings/disposition", "Disposition")
-create_subsection("./listings/development-safety-update-report", "Development Safety Update Report")
+create_subsection("./listings/development-safety-update-report", "Development Safety Update Report") # nolint
 create_subsection("./listings/ecg", "ECG")
 create_subsection("./listings/exposure", "Exposure")
 create_subsection("./listings/lab-results", "Lab Results")

From b7205e3186cfdb2abed76a161bee922461c9b369 Mon Sep 17 00:00:00 2001
From: ayogasekaram <abinaya.yogasekaram@contractors.roche.com>
Date: Thu, 21 Sep 2023 17:13:18 +0000
Subject: [PATCH 10/11] update index

---
 book/generate-index.R           |   6 +-
 book/listings/ADA/adal02.qmd    | 131 ----------------
 book/listings/ECG/egl01.qmd     |  89 -----------
 book/tables/ADA/adat01.qmd      | 230 ----------------------------
 book/tables/ADA/adat02.qmd      | 151 -------------------
 book/tables/ADA/adat03.qmd      | 131 ----------------
 book/tables/ADA/adat04a.qmd     | 240 ------------------------------
 book/tables/ADA/adat04b.qmd     | 223 ----------------------------
 book/tables/ECG/egt01.qmd       | 142 ------------------
 book/tables/ECG/egt02.qmd       | 126 ----------------
 book/tables/ECG/egt03.qmd       | 204 -------------------------
 book/tables/ECG/egt04.qmd       | 100 -------------
 book/tables/ECG/egt05_qtcat.qmd | 255 --------------------------------
 13 files changed, 3 insertions(+), 2025 deletions(-)
 delete mode 100644 book/listings/ADA/adal02.qmd
 delete mode 100644 book/listings/ECG/egl01.qmd
 delete mode 100644 book/tables/ADA/adat01.qmd
 delete mode 100644 book/tables/ADA/adat02.qmd
 delete mode 100644 book/tables/ADA/adat03.qmd
 delete mode 100644 book/tables/ADA/adat04a.qmd
 delete mode 100644 book/tables/ADA/adat04b.qmd
 delete mode 100644 book/tables/ECG/egt01.qmd
 delete mode 100644 book/tables/ECG/egt02.qmd
 delete mode 100644 book/tables/ECG/egt03.qmd
 delete mode 100644 book/tables/ECG/egt04.qmd
 delete mode 100644 book/tables/ECG/egt05_qtcat.qmd

diff --git a/book/generate-index.R b/book/generate-index.R
index 91176eeda3..927a0df5c8 100755
--- a/book/generate-index.R
+++ b/book/generate-index.R
@@ -44,7 +44,7 @@ create_subsection("./tables/deaths", "Deaths")
 create_subsection("./tables/demography", "Demography")
 create_subsection("./tables/disclosures", "Disclosures")
 create_subsection("./tables/disposition", "Disposition")
-create_subsection("./tables/ecg", "ECG")
+create_subsection("./tables/ECG", "ECG")
 create_subsection("./tables/efficacy", "Efficacy")
 create_subsection("./tables/exposure", "Exposure")
 create_subsection("./tables/lab-results", "Lab Results")
@@ -57,12 +57,12 @@ create_subsection("./tables/vital-signs", "Vital Signs")
 # Listings
 
 section_header("Listings")
-create_subsection("./listings/ada", "ADA")
+create_subsection("./listings/ADA", "ADA")
 create_subsection("./listings/adverse-events", "Adverse Events")
 create_subsection("./listings/concomitant-medications", "Concomitant Medications") # nolint
 create_subsection("./listings/disposition", "Disposition")
 create_subsection("./listings/development-safety-update-report", "Development Safety Update Report") # nolint
-create_subsection("./listings/ecg", "ECG")
+create_subsection("./listings/ECG", "ECG")
 create_subsection("./listings/exposure", "Exposure")
 create_subsection("./listings/lab-results", "Lab Results")
 create_subsection("./listings/medical-history", "Medical History")
diff --git a/book/listings/ADA/adal02.qmd b/book/listings/ADA/adal02.qmd
deleted file mode 100644
index 9ddcaf7f56..0000000000
--- a/book/listings/ADA/adal02.qmd
+++ /dev/null
@@ -1,131 +0,0 @@
----
-title: ADAL02
-subtitle: Listing of Anti-Drug Antibody Data for Treatment Emergent ADA Positive Patients
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(dplyr)
-library(rlistings)
-library(scda)
-
-adab <- synthetic_cdisc_dataset("latest", "adab") %>%
-  filter(NFRLT %% 1 == 0 & NFRLT > 0)
-
-trt <- "A: Drug X"
-drug_a <- "A: Drug X Antibody"
-drugcd <- unique(adab$PARAMCD[adab$PARAM == "Antibody titer units"])[1]
-min_titer <- 1.10
-
-adab_x <- adab %>%
-  filter(
-    ARM == trt,
-    PARCAT1 == drug_a,
-    ADPBLPFL == "Y"
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %>%
-  unique() %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  filter(if_any(matches("Treatment Emergent - Positive"), ~ .x == 1)) %>%
-  # filter(`Treatment Emergent - Positive` == 1) %>%
-  mutate(
-    VISN = factor(paste0(
-      VISIT, "\n(Day ",
-      ifelse(
-        NFRLT %% 1 == 0,
-        NFRLT,
-        as.character(format(round(NFRLT, 2), nsmall = 2))
-      ),
-      ")"
-    )),
-    PTES = ifelse(
-      ifelse("Treatment induced ADA" %in% names(.), `Treatment induced ADA` == 1, F),
-      ifelse(
-        "Transient ADA" %in% names(.) & `Transient ADA` == 1,
-        "Induced (Transient)",
-        "Induced (Persistent)"
-      ),
-      "Enhanced"
-    )
-  ) %>%
-  mutate(
-    AVAL = paste0(
-      ifelse(
-        ifelse("ADA interpreted per sample result" %in% names(.), `ADA interpreted per sample result` == 0, F),
-        "NEGATIVE",
-        ifelse(
-          ifelse("Antibody titer units" %in% names(.), !is.na(`Antibody titer units`), F),
-          ifelse(
-            `Antibody titer units` < min_titer,
-            paste0("<", format(min_titer, nsmall = 2)),
-            as.character(format(round(`Antibody titer units`, 2), nsmall = 2))
-          ),
-          "---"
-        )
-      ),
-      ifelse(
-        ifelse("NAB interpreted per sample result" %in% names(.), `NAB interpreted per sample result` == 1, F),
-        "*",
-        ""
-      )
-    )
-  )
-
-out <- adab_x %>%
-  select(USUBJID, VISN, AVAL, PTES) %>%
-  tidyr::pivot_wider(
-    names_from = VISN,
-    values_from = AVAL
-  ) %>%
-  select(USUBJID, unique(adab_x$VISN[order(adab_x$NFRLT)]), PTES)
-
-var_labels(out) <- names(out)
-
-out <- out %>%
-  var_relabel(
-    USUBJID = "Subject ID",
-    PTES = "Patient Treatment\nEmergent ADA Status"
-  )
-```
-
-## Standard Listing
-
-```{r lsting, test = list(lsting = "lsting")}
-lsting <- as_listing(
-  out,
-  key_cols = "USUBJID",
-  disp_cols = names(out),
-  main_title = paste0(
-    "Listing of Anti-", drugcd, " Antibody Data for Treatment Emergent ADA Positive Patients, PK Population",
-    "\nProtocol: ", unique(adab$PARCAT1)[1]
-  ),
-  subtitles = paste("\nTreatment Group:", trt),
-  main_footer = "Minimum reportable titer = 1.10 (example only)
---- = No sample evaluated
-ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies)
-Number of patients positive for Treatment Emergent ADA = the number of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.
-Treatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.
-Treatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
-Transient ADA = ADA positive result detected (a) at only one post-baseline sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints during treatment where the first and last ADA positive samples  are separated by a period of < 16 weeks, irrespective of any negative samples in between.
-Persistent ADA =  ADA positive result detected (a) at the last post-baseline sampling timepoint, OR (b) at 2 or more time points during treatment where the first and last ADA positive samples are separated by a period ≥ 16 weeks, irrespective of any negative samples in between.
-Asterisk denotes sample that tested positive for Neutralizing Antibodies."
-)
-
-head(lsting, 20)
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/listings/ECG/egl01.qmd b/book/listings/ECG/egl01.qmd
deleted file mode 100644
index a595b864c9..0000000000
--- a/book/listings/ECG/egl01.qmd
+++ /dev/null
@@ -1,89 +0,0 @@
----
-title: EGL01
-subtitle: 'Listing of ECG Data: Safety-Evaluable Patients'
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(dplyr)
-library(scda)
-library(rlistings)
-
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-get_param_unit_range <- function(dataset) {
-  u_rng <- lapply(unique(dataset$PARAMCD), function(p) {
-    dat <- dataset %>% filter(PARAMCD == p)
-    list(
-      unit = unique(dat$AVALU),
-      range = paste0(unique(dat$ANRLO), "-", unique(dat$ANRHI))
-    )
-  })
-  names(u_rng) <- unique(dataset$PARAMCD)
-  u_rng
-}
-
-eg_u_rng <- get_param_unit_range(adeg)
-
-adeg_sub <- adeg %>%
-  filter(!is.na(AVAL) & SAFFL == "Y" & ANL01FL == "Y" & !is.na(EGSEQ) & PARAMCD != "ECGINTP") %>%
-  mutate(
-    CRTNPT = paste(SITEID, sub("^.*-([[:alnum:]]+)$", "\\1", SUBJID), sep = "/"),
-    AGSXRC = paste(AGE, SEX, RACE, sep = "/"),
-    AVAL = format(round(AVAL, 2), nsmall = 2),
-    AVAL_ANRIND = ifelse(ANRIND %in% c("NORMAL", ""), AVAL, paste(AVAL, substr(ANRIND, 1, 1), sep = "/")),
-    CHG = format(round(CHG, 2), nsmall = 2)
-  )
-
-anl_eg <- adeg_sub %>%
-  select(SUBJID, CRTNPT, AGSXRC, TRT01A, PARAMCD, AVAL_ANRIND, CHG, ADY, AVISIT, ADTM) %>%
-  tidyr::pivot_wider(
-    id_cols = c(SUBJID, CRTNPT, AGSXRC, TRT01A, ADY, AVISIT, ADTM),
-    names_from = PARAMCD,
-    values_from = c(AVAL_ANRIND, CHG)
-  )
-
-out <- anl_eg %>%
-  select(CRTNPT, AGSXRC, TRT01A, AVISIT, ADY, AVAL_ANRIND_HR, CHG_HR, AVAL_ANRIND_QT, CHG_QT, AVAL_ANRIND_RR, CHG_RR) %>%
-  var_relabel(
-    CRTNPT = "Center/Subject ID",
-    AGSXRC = "Age/Sex/Race",
-    TRT01A = "Treatment",
-    AVISIT = "Visit",
-    ADY = "Study\nDay",
-    AVAL_ANRIND_HR = paste0("Heart Rate Result\n(", eg_u_rng$HR$unit, ");\nRange:(", eg_u_rng$HR$range, ")"),
-    CHG_HR = "Heart Rate\nChange from BL",
-    AVAL_ANRIND_QT = paste0("QT Duration Result\n(", eg_u_rng$QT$unit, ");\nRange:(", eg_u_rng$QT$range, ")"),
-    CHG_QT = "QT Duration\nChange from BL",
-    AVAL_ANRIND_RR = paste0("RR Duration Result\n(", eg_u_rng$RR$unit, ");\nRange:(", eg_u_rng$RR$range, ")"),
-    CHG_RR = "RR Duration\nChange from BL"
-  )
-```
-
-## Standard Listing
-
-```{r lsting, test = list(lsting = "lsting")}
-lsting <- as_listing(
-  out,
-  key_cols = c("CRTNPT", "AGSXRC", "TRT01A", "AVISIT", "ADY"),
-  disp_cols = names(out),
-  main_title = "Listing of ECG Data: Safety-Evaluable Patients",
-  main_footer = "Baseline is the patient's last observation prior to initiation of study drug. Abnormalities are flagged as high (H) or low (L) if
-  outside the Roche standard reference range."
-)
-
-head(lsting, 20)
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ADA/adat01.qmd b/book/tables/ADA/adat01.qmd
deleted file mode 100644
index 31a13bd819..0000000000
--- a/book/tables/ADA/adat01.qmd
+++ /dev/null
@@ -1,230 +0,0 @@
----
-title: ADAT01
-subtitle: Baseline Prevalence and Incidence of Treatment Emergent ADA
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(tern)
-library(dplyr)
-library(scda)
-library(tibble)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adab <- synthetic_cdisc_dataset("latest", "adab")
-
-combodf <- tribble(
-  ~valname, ~label, ~levelcombo, ~exargs,
-  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
-  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
-)
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adab <- adab %>%
-  filter(PARCAT1 == "A: Drug X Antibody") %>%
-  select(-ARRLT, -NRRLT)
-
-# Baseline Pts
-adab_b <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL == "Y",
-    ADABLPFL == "Y",
-    PARAM %in% c("ADA interpreted per sample result")
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU) %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  mutate_at(
-    c("ADA interpreted per sample result"),
-    as.logical
-  ) %>%
-  mutate(
-    ADABLPFL = ADABLPFL == "Y",
-    PADABLPFL = `ADA interpreted per sample result` == "TRUE",
-    NADABLPFL = `ADA interpreted per sample result` == "FALSE"
-  ) %>%
-  var_relabel(
-    ADABLPFL = "Baseline evaluable patients",
-    PADABLPFL = "Patient with a positive sample at baseline",
-    NADABLPFL = "Patient with no positive samples at baseline"
-  )
-
-# Post Baseline Treatment Enhanced NAb positive Pts
-adab_pb <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL != "Y",
-    ADPBLPFL == "Y",
-    PARAM %in% c(
-      "ADA interpreted per sample result",
-      "Treatment Emergent - Positive",
-      "Treatment induced ADA",
-      "Treatment enhanced ADA",
-      "Treatment Emergent - Negative",
-      "Treatment unaffected"
-    )
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU) %>%
-  unique() %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  mutate(
-    across(
-      any_of(c(
-        "ADA interpreted per sample result", "Treatment Emergent - Positive",
-        "Treatment induced ADA", "Treatment enhanced ADA",
-        "Treatment Emergent - Negative", "Treatment unaffected"
-      )),
-      as.logical
-    )
-  ) %>%
-  mutate(
-    ADPBLPFL = ADPBLPFL == "Y",
-    PTEFL = if (exists("Treatment Emergent - Positive", where = .)) `Treatment Emergent - Positive` == "TRUE" else FALSE,
-    TIFL = if (exists("Treatment induced ADA", where = .)) `Treatment induced ADA` == "TRUE" else FALSE,
-    TEFL = if (exists("Treatment enhanced ADA", where = .)) `Treatment enhanced ADA` == "TRUE" else FALSE,
-    NTEFL = if (exists("Treatment Emergent - Negative", where = .)) `Treatment Emergent - Negative` == "TRUE" else FALSE,
-    TUFL = if (exists("Treatment unaffected", where = .)) `Treatment unaffected` == "TRUE" else FALSE
-  ) %>%
-  var_relabel(
-    ADPBLPFL = "Post-baseline evaluable patients",
-    PTEFL = "Patient positive for Treatment Emergent ADA",
-    TIFL = "Treatment-induced ADA",
-    TEFL = "Treatment-enhanced ADA",
-    NTEFL = "Patient negative for Treatment Emergent ADA",
-    TUFL = "Treatment unaffected"
-  )
-```
-
-## Standard Table
-
-```{r variant1, test = list(result_v1 = "result")}
-# Layout for Baseline Prevalence of NAbs
-lyt_bl <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = add_combo_levels(combodf)
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "ADABLPFL",
-    .stats = "count",
-    var_labels = "Baseline Prevalence of ADAs",
-    show_labels = "visible",
-    table_names = "t1"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "PADABLPFL",
-    table_names = "t2",
-    .indent_mods = 1L,
-    var_labels = "a",
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NADABLPFL",
-    .stats = "count",
-    show_labels = "hidden",
-    .indent_mods = 1L,
-    table_names = "t3"
-  )
-
-# Layout for incidence of NAbs
-lyt_pb <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = add_combo_levels(combodf)
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "ADPBLPFL",
-    .stats = "count",
-    var_labels = "Incidence of Treatment Emergent ADAs",
-    show_labels = "visible",
-    table_names = "tb1"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "PTEFL",
-    table_names = "tb2",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("TIFL", "TEFL"),
-    .stats = "count",
-    table_names = "tb3",
-    .indent_mods = 2L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NTEFL",
-    table_names = "tb4",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "TUFL",
-    .stats = "count",
-    table_names = "tb5",
-    .indent_mods = 2L,
-    show_labels = "hidden"
-  )
-
-result_1 <- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)
-result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
-
-# Combine tables.
-result_1@col_info <- result_2@col_info
-result <- rbind(result_1, result_2)
-
-# Change the column order.
-result <- cbind_rtables(result[, 1], result[, 3]) %>%
-  cbind_rtables(result[, 4]) %>%
-  cbind_rtables(result[, 2]) %>%
-  cbind_rtables(result[, 5])
-
-main_title(result) <- paste(
-  "Baseline Prevalence and Incidence of Treatment Emergent ADA"
-)
-main_footer(result) <- paste(
-  "ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient = a patient with an ADA assay result from a baseline sample(s)
-  Post-baseline evaluable patient = a patient with an ADA assay result from at least one post-baseline sample Number of patients positive for Treatment Emergent
-  ADA = the number (and percentage) of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.
-  Treatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.
-  Treatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
-  Number of patients negative for Treatment Emergent ADA = number of post-baseline evaluable patients with negative or missing baseline ADA result(s) and all negative post-baseline results, or a patient who is treatment unaffected.
-  Treatment unaffected = A post-baseline evaluable patient with a positive ADA result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing.
-  For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer."
-)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| code-fold: show
-
-# In progress
-```
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ADA/adat02.qmd b/book/tables/ADA/adat02.qmd
deleted file mode 100644
index 630e8e817e..0000000000
--- a/book/tables/ADA/adat02.qmd
+++ /dev/null
@@ -1,151 +0,0 @@
----
-title: ADAT02
-subtitle: Summary of Patients with Treatment-Induced ADA
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(tern)
-library(dplyr)
-library(scda)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adab <- synthetic_cdisc_dataset("latest", "adab")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adab <- df_explicit_na(adab) %>%
-  mutate(ADPBLPFL = "Y") %>% # temp fix
-  filter(
-    ADPBLPFL == "Y",
-    !PARAM %in% c(
-      "NAB interpreted per sample result",
-      "NAB Status of a patient",
-      "Treatment enhanced ADA"
-    )
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %>%
-  unique() %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  mutate(
-    across(
-      any_of(c(
-        "Treatment induced ADA",
-        "Transient ADA",
-        "Persistent ADA"
-      )),
-      as.logical
-    )
-  ) %>%
-  mutate(
-    ADPBLPFL = ADPBLPFL == "Y",
-    TI_ADA = if (exists("Treatment induced ADA", where = .)) `Treatment induced ADA` else FALSE
-  ) %>%
-  var_relabel(
-    ADPBLPFL = "Post-baseline evaluable patients",
-    TI_ADA = "Treatment-induced ADA patients"
-  )
-
-adab_ti <- adab %>%
-  filter(TI_ADA)
-```
-
-## Standard Table
-
-```{r variant1, test = list(result_v1 = "result")}
-# Layout for post-baseline evaluable patient variables from adab dataset.
-lyt_adab <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = drop_split_levels
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "ADPBLPFL",
-    .stats = "count",
-    table_names = "post_baseline"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "TI_ADA"
-  )
-
-# Layout for treatment-induced patient variables from adab dataset.
-lyt_adab_ti <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = keep_split_levels(c("A: Drug X", "C: Combination", "<Missing>")) # temp fix
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("Transient ADA", "Persistent ADA"),
-    var_labels = "Treatment-induced ADA patients with",
-    show_labels = "visible"
-  ) %>%
-  analyze_vars(
-    "Time to onset of ADA",
-    .stats = "median",
-    nested = FALSE,
-    .labels = c(median = "Median time to onset of ADA (weeks)")
-  ) %>%
-  analyze_vars(
-    "Antibody titer units",
-    .stats = "range",
-    nested = FALSE,
-    .labels = c(range = "ADA titer range (min - max)")
-  )
-
-result_adab <- build_table(lyt_adab, df = adab, alt_counts_df = adsl)
-result_adab_ti <- build_table(lyt_adab_ti, df = adab_ti, alt_counts_df = adsl)
-
-# Combine tables.
-col_info(result_adab) <- col_info(result_adab_ti)
-result <- rbind(
-  result_adab,
-  result_adab_ti
-)
-
-main_title(result) <- paste(
-  "Summary of Patients with Treatment-Induced ADA, PK Population"
-)
-subtitles(result) <- paste("Protocol:", unique(adab$PARCAT1)[1])
-main_footer(result) <- paste(
-  "ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic
-  Antibodies)
-  Treatment-induced ADA = negative or missing baseline.
-  ADA result(s) and at least one positive post-baseline ADA result.
-  Transient ADA = ADA positive result detected (a) at only one post-baseline
-  sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints
-  during treatment where the first and last ADA positive samples are separated
-  by a period of < 16 weeks, irrespective of any negative samples in between.
-  Persistent ADA = ADA positive result detected (a) at the last post-baseline
-  sampling timepoint, OR (b) at 2 or more time points during treatment where
-  the first and last ADA positive samples are separated by a period ≥ 16
-  weeks, irrespective of any negative samples in between."
-)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| code-fold: show
-
-# In progress
-```
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ADA/adat03.qmd b/book/tables/ADA/adat03.qmd
deleted file mode 100644
index 561c5ab1af..0000000000
--- a/book/tables/ADA/adat03.qmd
+++ /dev/null
@@ -1,131 +0,0 @@
----
-title: ADAT03
-subtitle: Summary of Serum Concentrations at Timepoints Where ADA Samples Were Collected and Analyzed
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(dplyr)
-library(scda)
-library(tern)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adab <- synthetic_cdisc_dataset("latest", "adab")
-adpc <- synthetic_cdisc_dataset("latest", "adpc")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adab <- df_explicit_na(adab)
-adpc <- df_explicit_na(adpc)
-
-# Adjust zzz parameter
-max_conc <- 15
-
-adpc <- adpc %>% select(USUBJID, NFRLT, AVAL, AVALU, AVALCAT1)
-
-anl <- adab %>%
-  filter(., PARAM == "ADA interpreted per sample result") %>%
-  select(-AVAL, AVALC, AVALU)
-
-anl <- merge(anl, adpc, by = c("USUBJID", "NFRLT")) %>%
-  mutate(AVAL_LT = ifelse(AVAL <= max_conc, TRUE, FALSE))
-```
-
-## Standard Table (μg/mL)
-
-```{r variant1, test = list(result_v1 = "result")}
-# parameters in columns
-adat03_stats <- c("n", "mean", "sd", "median", "min", "max", "cv", "geom_mean", "count_fraction")
-adat03_lbls <- c(
-  n = "Total Number\nof Measurable\n Samples",
-  mean = "Mean",
-  sd = "SD",
-  median = "Median",
-  min = "Minimum",
-  max = "Maximum",
-  cv = "CV (%)",
-  geom_mean = "Geometric Mean",
-  count_fraction = paste0("Samples with\nConcentration\n≤ ", max_conc, "μg/mL")
-)
-adat03_fmts <- c(
-  n = "xx.",
-  mean = sprintf_format("%.3e"),
-  sd = sprintf_format("%.3e"),
-  median = sprintf_format("%.3e"),
-  min = sprintf_format("%.3e"),
-  max = sprintf_format("%.3e"),
-  cv = "xx.x",
-  geom_mean = sprintf_format("%.3e"),
-  count_fraction = format_count_fraction
-)
-
-afun_list <- lapply(
-  1:9,
-  function(i) make_afun(s_summary, .stats = adat03_stats[i], .formats = adat03_fmts[i], .labels = "Overall")
-)
-
-# lyt creation
-lyt <- basic_table() %>%
-  split_rows_by(
-    var = "ARM",
-    label_pos = "topleft",
-    split_label = "Treatment Group",
-    split_fun = drop_split_levels,
-    section_div = ""
-  ) %>%
-  add_rowcounts() %>%
-  split_rows_by(
-    var = "VISIT",
-    label_pos = "topleft",
-    split_label = "Visit",
-    split_fun = drop_split_levels,
-    child_labels = "hidden"
-  ) %>%
-  analyze_vars_in_cols(
-    vars = c(rep("AVAL", 8), "AVAL_LT"),
-    .stats = adat03_stats,
-    .labels = adat03_lbls,
-    .formats = adat03_fmts
-  ) %>%
-  analyze_colvars(
-    afun_list,
-    nested = FALSE,
-    extra_args = list(".labels" = "Overall")
-  )
-
-result <- build_table(lyt, anl, alt_counts_df = adsl)
-
-main_title(result) <- paste(
-  "Summary of Serum Concentrations (μg/mL) at Timepoints Where ADA Samples Were Collected and Analyzed\n
-  Protocol:", unique(adab$PARCAT1)[1]
-)
-subtitles(result) <- paste("Analyte:", unique(adab$PARAMCD)[1])
-fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL")) <- "Refers to the total no. of measurable ADA samples that have a corresponding measurable drug concentration sample (i.e. results with
-valid numeric values and LTRs). LTR results on post-dose samples are replaced by aaa µg/mL i.e. half of MQC value."
-fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL_LT")) <- "In validation, the assay was able to detect yyy ng/mL of surrogate ADA in the presence of zzz µg/mL of [drug]. BLQ = Below Limit of
-Quantitation, LTR = Lower than Reportable, MQC = Minimum Quantifiable Concentration, ADA = Anti-Drug Antibodies (is also referred to as ATA,
-or Anti-Therapeutic Antibodies). RXXXXXXX is also known as [drug]"
-
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| code-fold: show
-
-# In progress
-```
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ADA/adat04a.qmd b/book/tables/ADA/adat04a.qmd
deleted file mode 100644
index 3a3f56d998..0000000000
--- a/book/tables/ADA/adat04a.qmd
+++ /dev/null
@@ -1,240 +0,0 @@
----
-title: ADAT04A
-subtitle: Baseline Prevalence and Incidence of Treatment Emergent NAbs
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(tern)
-library(dplyr)
-library(scda)
-library(tibble)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adab <- synthetic_cdisc_dataset("latest", "adab")
-
-combodf <- tribble(
-  ~valname, ~label, ~levelcombo, ~exargs,
-  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
-  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
-)
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-
-# Baseline Pts
-adab_b <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL == "Y",
-    ADABLPFL == "Y",
-    PARAM %in% c(
-      "ADA interpreted per sample result",
-      "NAB interpreted per sample result"
-    )
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU) %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  mutate(
-    across(
-      any_of(c(
-        "ADA interpreted per sample result",
-        "NAB interpreted per sample result"
-      )),
-      as.logical
-    )
-  ) %>%
-  mutate(
-    ADABLPFL = ADABLPFL == "Y",
-    PADABLPFL = if (exists("ADA interpreted per sample result", where = .)) `ADA interpreted per sample result` == "TRUE" else FALSE,
-    PNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "TRUE" else FALSE,
-    NNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "FALSE" else FALSE
-  ) %>%
-  var_relabel(
-    ADABLPFL = "Baseline evaluable patients for ADA",
-    PADABLPFL = "Patients with a positive ADA sample at baseline",
-    PNABBLFL = "Patients with a positive NAb sample at baseline",
-    NNABBLFL = "Patient with no positive NAb samples at baseline"
-  )
-
-# Post Baseline Treatment Enhanced NAb positive Pts
-adab_pb <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL != "Y",
-    ADPBLPFL == "Y",
-    PARAM %in% c(
-      "ADA interpreted per sample result",
-      "Treatment Emergent - Positive, Neutralizing Antibody",
-      "Treatment induced ADA, Neutralizing Antibody",
-      "Treatment enhanced ADA, Neutralizing Antibody",
-      "NAB interpreted per sample result",
-      "Treatment unaffected, Neutralizing Antibody"
-    )
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %>%
-  unique() %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  mutate(
-    across(
-      any_of(c(
-        "ADA interpreted per sample result", "NAB interpreted per sample result",
-        "Treatment Emergent - Positive, Neutralizing Antibody",
-        "Treatment induced ADA, Neutralizing Antibody",
-        "Treatment enhanced ADA, Neutralizing Antibody",
-        "Treatment unaffected, Neutralizing Antibody"
-      )),
-      as.logical
-    )
-  ) %>%
-  mutate(
-    ADPBLPFL = ADPBLPFL == "Y",
-    ADAPFL = if (exists("ADA interpreted per sample result", where = .)) `ADA interpreted per sample result` == "TRUE" else FALSE,
-    TENABPFL = if (exists("Treatment Emergent - Positive, Neutralizing Antibody", where = .)) `Treatment Emergent - Positive, Neutralizing Antibody` == "TRUE" else FALSE,
-    TINPBFL = if (exists("Treatment induced ADA, Neutralizing Antibody", where = .)) `Treatment induced ADA, Neutralizing Antibody` == "TRUE" else FALSE,
-    TENPBFL = if (exists("Treatment enhanced ADA, Neutralizing Antibody", where = .)) `Treatment enhanced ADA, Neutralizing Antibody` == "TRUE" else FALSE,
-    NABNFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "FALSE" else FALSE,
-    TUNPBFL = if (exists("Treatment unaffected, Neutralizing Antibody", where = .)) `Treatment unaffected, Neutralizing Antibody` == "TRUE" else FALSE
-  ) %>%
-  var_relabel(
-    ADPBLPFL = "Post-baseline evaluable patients for ADA",
-    ADAPFL = "Patients positive for ADA",
-    TENABPFL = "Patients positive for Treatment Emergent NAb",
-    TINPBFL = "Treatment-induced NAb",
-    TENPBFL = "Treatment-enhanced NAb",
-    NABNFL = "Patients negative for NAb",
-    TUNPBFL = "Treatment unaffected"
-  )
-```
-
-## Summary of Treatment Emergent NAbs
-
-```{r variant1, test = list(result_v1 = "result")}
-# Layout for Baseline Prevalence of NAbs
-lyt_bl <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = add_combo_levels(combodf)
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("ADABLPFL", "PADABLPFL"),
-    table_names = "t1",
-    .stats = "count",
-    var_labels = "Baseline Prevalence of NAbs",
-    show_labels = "visible"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "PNABBLFL",
-    table_names = "t2",
-    .indent_mods = 1L
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NNABBLFL",
-    .stats = "count",
-    table_names = "t3",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  )
-
-# Layout for incidence of NAbs
-lyt_pb <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = add_combo_levels(combodf)
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("ADPBLPFL", "ADAPFL"),
-    .stats = "count",
-    var_labels = "Incidence of Treatment Emergent NAbs",
-    show_labels = "visible",
-    table_names = "tb1"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "TENABPFL",
-    table_names = "tb2",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("TINPBFL", "TENPBFL"),
-    .stats = "count",
-    table_names = "tb3",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NABNFL",
-    table_names = "tb4",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "TUNPBFL",
-    .stats = "count",
-    table_names = "tb5",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  )
-
-result_1 <- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)
-result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
-
-# Combine tables
-result <- rbind(result_1, result_2)
-
-# Change the column order
-result <- cbind_rtables(result[, 1], result[, 3]) %>%
-  cbind_rtables(result[, 4]) %>%
-  cbind_rtables(result[, 2]) %>%
-  cbind_rtables(result[, 5])
-
-main_title(result) <- paste(
-  "Baseline Prevalence and Incidence of Treatment Emergent NAbs"
-)
-subtitles(result) <- paste("Protocol:", unique(adab$PARCAT1)[1])
-main_footer(result) <- paste(
-  "NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s)
-  Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s)
-  Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample
-  Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample
-  Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.
-Number of patients positive for Treatment Emergent NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have treatment-induced NAb or treatment-enhanced NAb during the study period.
-Treatment-induced = a patient with negative or missing baseline result(s) and at least one positive post-baseline result. Treatment-enhanced = a patient with positive result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
-Number of patients negative for Treatment Emergent NAb = number of post-baseline evaluable patients with negative or missing baseline NAb result(s) and all negative post-baseline NAb results, or a patient who is NAb treatment unaffected.
-Treatment unaffected = A post-baseline evaluable patient with a positive result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing. For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer."
-)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| code-fold: show
-
-# In progress
-```
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ADA/adat04b.qmd b/book/tables/ADA/adat04b.qmd
deleted file mode 100644
index 5994f04002..0000000000
--- a/book/tables/ADA/adat04b.qmd
+++ /dev/null
@@ -1,223 +0,0 @@
----
-title: ADAT04B
-subtitle: Baseline Prevalence and Incidence of NAbs
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(tern)
-library(dplyr)
-library(scda)
-library(tibble)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adab <- synthetic_cdisc_dataset("latest", "adab")
-
-combodf <- tribble(
-  ~valname, ~label, ~levelcombo, ~exargs,
-  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
-  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
-)
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-
-# Baseline Pts
-adab_b <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL == "Y",
-    ADABLPFL == "Y",
-    PARAM %in% c(
-      "ADA interpreted per sample result",
-      "NAB interpreted per sample result"
-    )
-  ) %>%
-  select(-PARAMCD, -AVALC, -AVALU) %>%
-  tidyr::pivot_wider(
-    names_from = PARAM,
-    values_from = AVAL
-  ) %>%
-  mutate(
-    across(
-      any_of(c(
-        "ADA interpreted per sample result",
-        "NAB interpreted per sample result"
-      )),
-      as.logical
-    )
-  ) %>%
-  mutate(
-    ADABLPFL = ADABLPFL == "Y",
-    PADABLPFL = if (exists("ADA interpreted per sample result", where = .)) `ADA interpreted per sample result` == "TRUE" else FALSE,
-    PNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "TRUE" else FALSE,
-    NNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "FALSE" else FALSE
-  ) %>%
-  var_relabel(
-    ADABLPFL = "Baseline evaluable patients for ADA",
-    PADABLPFL = "Patients with a positive ADA sample at baseline",
-    PNABBLFL = "Patients with a positive NAb sample at baseline",
-    NNABBLFL = "Patients with no positive NAb sample at baseline"
-  )
-
-# Post-Baseline Evaluable Pts
-adab_pb_ada <- df_explicit_na(adab) %>%
-  filter(ADPBLPFL == "Y") %>%
-  select(STUDYID, USUBJID, ARM, ACTARM, ADPBLPFL) %>%
-  mutate(ADPBLPFL = ADPBLPFL == "Y") %>%
-  distinct()
-
-# Post-Baseline ADA Positive Pts
-adab_pb_adap <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL != "Y",
-    PARAM == "ADA interpreted per sample result",
-    AVALC == "POSITIVE"
-  ) %>%
-  mutate(ADAPFL = AVALC == "POSITIVE") %>%
-  select(STUDYID, USUBJID, ARM, ACTARM, ADAPFL) %>%
-  distinct()
-
-# Post-Baseline NAb Positive Pts
-adab_pb_nabp <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL != "Y",
-    PARAM == "NAb interpreted per sample result",
-    AVALC == "POSITIVE"
-  ) %>%
-  mutate(NABPFL = AVALC == "POSITIVE") %>%
-  select(STUDYID, USUBJID, ARM, ACTARM, NABPFL) %>%
-  distinct()
-
-# Post-Baseline NAb Negative Pts
-adab_pb_nabn <- df_explicit_na(adab) %>%
-  filter(
-    ABLFL != "Y",
-    PARAM == "NAb interpreted per sample result",
-    AVALC == "NEGATIVE"
-  ) %>%
-  rename(NABNFL = AVALC) %>%
-  select(STUDYID, USUBJID, ARM, ACTARM, NABNFL) %>%
-  distinct()
-
-mergecol <- c("STUDYID", "USUBJID", "ARM", "ACTARM")
-
-adab_pb <- left_join(adab_pb_ada, adab_pb_adap, by = mergecol) %>%
-  left_join(adab_pb_nabp, by = mergecol) %>%
-  mutate(
-    NABNFL = ifelse(is.na(NABPFL), "TRUE", "FALSE"),
-    NABNFL = NABNFL == "TRUE"
-  ) %>%
-  var_relabel(
-    ADPBLPFL = "Post-baseline evaluable patients for ADA",
-    ADAPFL = "Patients positive for ADA",
-    NABPFL = "Patients positive for NAb",
-    NABNFL = "Patients negative for NAb"
-  )
-```
-
-## Standard Table
-
-```{r variant1, test = list(result_v1 = "result")}
-# Layout for Baseline Prevalence of NAbs
-lyt_bl <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = add_combo_levels(combodf)
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("ADABLPFL", "PADABLPFL"),
-    .stats = "count",
-    var_labels = "Baseline Prevalence of NAbs",
-    show_labels = "visible",
-    table_names = "t1"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "PNABBLFL",
-    table_names = "t2",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NNABBLFL",
-    .stats = "count",
-    table_names = "t3",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  )
-
-# Layout for incidence of NAbs
-lyt_pb <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(
-    "ACTARM",
-    split_fun = add_combo_levels(combodf)
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = c("ADPBLPFL", "ADAPFL"),
-    .stats = "count",
-    var_labels = "Incidence of NAbs",
-    show_labels = "visible",
-    table_names = "tb1"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NABPFL",
-    table_names = "tb2",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  ) %>%
-  count_patients_with_flags(
-    "USUBJID",
-    flag_variables = "NABNFL",
-    .stats = "count",
-    table_names = "tb3",
-    .indent_mods = 1L,
-    show_labels = "hidden"
-  )
-
-result_1 <- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)
-result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
-
-# Combine tables.
-result <- rbind(result_1, result_2)
-
-# Change the column order.
-result <- cbind_rtables(result[, 1], result[, 3]) %>%
-  cbind_rtables(result[, 4]) %>%
-  cbind_rtables(result[, 2]) %>%
-  cbind_rtables(result[, 5])
-
-main_title(result) <- paste(
-  "Baseline Prevalence and Incidence of Neutralizing Antibodies (NAbs)"
-)
-subtitles(result) <- paste("Protocol:", unique(adab$PARCAT1)[1])
-main_footer(result) <- paste(
-  "NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s) Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s) Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.
-Number of patients positive for NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have at least one positive post-baseline NAb result during the study period. Number of patients negative for NAb = number of post-baseline evaluable patients with all negative post-baseline NAb results."
-)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| code-fold: show
-
-# In progress
-```
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ECG/egt01.qmd b/book/tables/ECG/egt01.qmd
deleted file mode 100644
index 017188e635..0000000000
--- a/book/tables/ECG/egt01.qmd
+++ /dev/null
@@ -1,142 +0,0 @@
----
-title: EGT01
-subtitle: ECG Results and Change from Baseline by Visit
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(dplyr)
-library(tern)
-library(scda)
-
-# Data should be filtered for the studied Parameter (`PARAM`) and the
-# Analysis Visit (`AVISIT`). According to the GDSR template, the values for
-# the `AVISIT` reported in the EGT01 standard may be:
-# 'POST-BASELINE MAXIMUM', 'POST-BASELINE MINIMUM', 'POST-BASELINE LAST'.
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adeg <- df_explicit_na(adeg)
-
-adeg_f <- adeg %>%
-  filter(ANL01FL == "Y") %>%
-  filter(PARAM %in% c("Heart Rate", "QT Duration", "RR Duration"))
-```
-
-## Standard Table
-
-```{r variant1, test = list(result_v1 = "result")}
-# Define the split function
-split_fun <- drop_split_levels
-
-afun <- function(x, .var, .spl_context, ...) {
-  n_fun <- sum(!is.na(x), na.rm = TRUE)
-  if (n_fun == 0) {
-    mean_sd_fun <- c(NA, NA)
-    median_fun <- NA
-    min_max_fun <- c(NA, NA)
-  } else {
-    mean_sd_fun <- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))
-    median_fun <- median(x, na.rm = TRUE)
-    min_max_fun <- c(min(x), max(x))
-  }
-  is_chg <- .var == "CHG"
-  is_baseline <- .spl_context$value[which(.spl_context$split == "AVISIT")] == "BASELINE"
-  if (is_baseline && is_chg) n_fun <- mean_sd_fun <- median_fun <- min_max_fun <- NULL
-
-  in_rows(
-    "n" = n_fun,
-    "Mean (SD)" = mean_sd_fun,
-    "Median" = median_fun,
-    "Min - Max" = min_max_fun,
-    .formats = list("n" = "xx", "Mean (SD)" = "xx.xx (xx.xx)", "Median" = "xx.xx", "Min - Max" = "xx.xx - xx.xx"),
-    .format_na_strs = list("n" = "NE", "Mean (SD)" = "NE (NE)", "Median" = "NE", "Min - Max" = "NE - NE")
-  )
-}
-
-lyt <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by("ACTARM") %>%
-  split_rows_by("PARAM", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f$PARAM)) %>%
-  split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f$AVISIT)) %>%
-  split_cols_by_multivar(
-    vars = c("AVAL", "CHG"),
-    varlabels = c("Value at Visit", "Change from\nBaseline")
-  ) %>%
-  analyze_colvars(afun = afun)
-
-result <- build_table(lyt, adeg_f, alt_counts_df = adsl)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| screenshot.opts = list(delay = 16)
-
-library(teal.modules.clinical)
-library(scda)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adeg <- df_explicit_na(adeg)
-
-app <- init(
-  data = cdisc_data(
-    cdisc_dataset("ADSL",
-      adsl,
-      code = 'ADSL <- df_explicit_na(synthetic_cdisc_dataset("latest", "adsl"))'
-    ),
-    cdisc_dataset("ADEG",
-      adeg,
-      code = 'ADEG <- df_explicit_na(synthetic_cdisc_dataset("latest", "adeg"))'
-    ),
-    check = TRUE
-  ),
-  modules = modules(
-    tm_t_summary_by(
-      label = "ECG Results and Change from Baseline by Visit",
-      dataname = "ADEG",
-      arm_var = choices_selected(
-        choices = variable_choices(adsl, c("ARM", "ARMCD")),
-        selected = "ARM"
-      ),
-      by_vars = choices_selected(
-        choices = variable_choices(adeg, c("PARAM", "AVISIT")),
-        selected = c("AVISIT")
-      ),
-      summarize_vars = choices_selected(
-        choices = variable_choices(adeg, c("AVAL", "CHG")),
-        selected = c("AVAL")
-      ),
-      useNA = "ifany",
-      paramcd = choices_selected(
-        choices = value_choices(adeg, "PARAMCD", "PARAM"),
-        selected = "HR"
-      ),
-      parallel_vars = TRUE
-    )
-  ),
-  filter = list(ADEG = list(AVAL = list()))
-)
-
-shinyApp(app$ui, app$server)
-```
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ECG/egt02.qmd b/book/tables/ECG/egt02.qmd
deleted file mode 100644
index addbda506f..0000000000
--- a/book/tables/ECG/egt02.qmd
+++ /dev/null
@@ -1,126 +0,0 @@
----
-title: EGT02
-subtitle: ECG Abnormalities (EGT02_1 & EGT02_2)
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(dplyr)
-library(scda)
-library(tern)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adeg <- df_explicit_na(adeg)
-
-# Note:  We keep only post-baseline for analysis.
-adeg_f <- adeg %>%
-  filter(ONTRTFL == "Y") %>%
-  filter(PARAM %in% c("Heart Rate", "QT Duration", "RR Duration")) %>%
-  filter(ANRIND != "<Missing>") %>%
-  var_relabel(
-    PARAM = "Assessment",
-    ANRIND = "Abnormality"
-  )
-```
-
-## ECG Abnormalities Regardless <br/> of Abnormality at Baseline
-
-```{r variant1, test = list(result_v1 = "result")}
-split_fun <- drop_split_levels
-
-lyt <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(var = "ACTARM") %>%
-  split_rows_by(
-    "PARAM",
-    split_fun = split_fun,
-    label_pos = "topleft",
-    split_label = obj_label(adeg_f$PARAM)
-  ) %>%
-  count_abnormal("ANRIND", abnormal = list(Low = "LOW", High = "HIGH"), exclude_base_abn = FALSE) %>%
-  append_varlabels(adeg_f, "ANRIND", indent = 1L)
-
-result <- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl)
-result
-```
-
-## ECG Abnormalities Among Subjects <br/> Without Abnormality at Baseline
-
-```{r variant2, test = list(result_v2 = "result")}
-split_fun <- drop_split_levels
-
-lyt <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by(var = "ACTARM") %>%
-  split_rows_by(
-    "PARAM",
-    split_fun = split_fun,
-    label_pos = "topleft",
-    split_label = obj_label(adeg_f$PARAM)
-  ) %>%
-  count_abnormal("ANRIND", abnormal = list(Low = "LOW", High = "HIGH"), exclude_base_abn = TRUE) %>%
-  append_varlabels(adeg_f, "ANRIND", indent = 1L)
-
-result <- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| screenshot.opts = list(delay = 18)
-
-library(scda)
-library(teal.modules.clinical)
-library(dplyr)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-app <- init(
-  data = cdisc_data(
-    cdisc_dataset("ADSL", adsl, code = 'ADSL <- synthetic_cdisc_dataset("latest", "adsl")'),
-    cdisc_dataset("ADEG", adeg, code = 'ADEG <- synthetic_cdisc_dataset("latest", "adeg")'),
-    check = TRUE
-  ),
-  modules = modules(
-    tm_t_abnormality(
-      label = "Abnormality Table",
-      dataname = "ADEG",
-      arm_var = choices_selected(
-        choices = variable_choices(adsl, subset = c("ARM", "ARMCD")),
-        selected = "ARM"
-      ),
-      by_vars = choices_selected(
-        choices = variable_choices(adeg, subset = c("EGCAT", "PARAM", "AVISIT")),
-        selected = c("PARAM"),
-        keep_order = TRUE
-      ),
-      grade = choices_selected(
-        choices = variable_choices(adeg, subset = "ANRIND"),
-        selected = "ANRIND",
-        fixed = TRUE
-      ),
-      abnormal = list(Low = "LOW", High = "HIGH"),
-      exclude_base_abn = FALSE
-    )
-  )
-)
-
-shinyApp(app$ui, app$server)
-```
-
-{{< include ../../repro.qmd >}}
-:::
diff --git a/book/tables/ECG/egt03.qmd b/book/tables/ECG/egt03.qmd
deleted file mode 100644
index 6d335eb12e..0000000000
--- a/book/tables/ECG/egt03.qmd
+++ /dev/null
@@ -1,204 +0,0 @@
----
-title: EGT03
-subtitle: Shift Table of ECG Interval Data -- Baseline Versus Minimum/Maximum Post-Baseline
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(scda)
-library(tern)
-library(dplyr)
-
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adeg <- df_explicit_na(adeg)
-
-adeg_labels <- var_labels(adeg)
-
-adeg_f_pbmin <- subset(
-  adeg,
-  PARAMCD == "HR" & # Heart Rate
-    SAFFL == "Y" & # "Safety Population Flag"
-    ONTRTFL == "Y" & # "On Treatment Record Flag"
-    AVISIT == "POST-BASELINE MINIMUM" # "Analysis Visit"
-)
-
-adeg_f_pbmax <- subset(
-  adeg,
-  PARAMCD == "HR" & # Heart Rate
-    SAFFL == "Y" & # "Safety Population Flag"
-    ONTRTFL == "Y" & # "On Treatment Record Flag"
-    AVISIT == "POST-BASELINE MAXIMUM" # "Analysis Visit"
-)
-
-var_labels(adeg_f_pbmin) <- adeg_labels
-var_labels(adeg_f_pbmax) <- adeg_labels
-```
-
-## Table of Baseline Versus <br/> Minimum Post-Baseline
-
-For the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N.
-For illustration purposes, missing data are added to the example dataset.
-
-```{r variant1, test = list(result_v1 = "result")}
-set.seed(123, kind = "Mersenne-Twister")
-
-adeg_f_pbmin$BNRIND <- factor(
-  adeg_f_pbmin$BNRIND,
-  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
-  labels = c("LOW", "NORMAL", "HIGH", "Missing")
-)
-adeg_f_pbmin$ANRIND <- factor(
-  adeg_f_pbmin$ANRIND,
-  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
-  labels = c("LOW", "NORMAL", "HIGH", "Missing")
-)
-
-adeg_f_pbmin$BNRIND[sample(1:nrow(adeg_f_pbmin), size = 5)] <- "Missing"
-adeg_f_pbmin$ANRIND[sample(1:nrow(adeg_f_pbmin), size = 5)] <- "Missing"
-
-attr(adeg_f_pbmin$ANRIND, "label") <- "Analysis Reference Range Indicator"
-attr(adeg_f_pbmin$BNRIND, "label") <- "Baseline Reference Range Indicator"
-
-# Temporary solution for overarching column
-adeg_f_pbmin <- adeg_f_pbmin %>% mutate(min_label = "Minimum Post-Baseline Assessment")
-
-# Define the split function
-split_fun <- drop_split_levels
-
-lyt <- basic_table() %>%
-  split_cols_by("min_label") %>%
-  split_cols_by("ANRIND") %>%
-  split_rows_by("ARMCD", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f_pbmin$ARMCD)) %>%
-  add_rowcounts() %>%
-  analyze_vars("BNRIND", denom = "N_row", .stats = "count_fraction") %>%
-  append_varlabels(adeg_f_pbmin, c("BNRIND"), indent = 1L)
-
-result <- build_table(
-  lyt = lyt,
-  df = adeg_f_pbmin
-)
-
-result
-```
-
-## Table of Baseline Versus <br/> Maximum Post-Baseline
-
-For the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N.
-For illustration purpose, missing data are added to the example dataset.
-
-```{r variant2, test = list(result_v2 = "result")}
-set.seed(123, kind = "Mersenne-Twister")
-
-adeg_f_pbmax$BNRIND <- factor(
-  adeg_f_pbmax$BNRIND,
-  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
-  labels = c("LOW", "NORMAL", "HIGH", "Missing")
-)
-adeg_f_pbmax$ANRIND <- factor(
-  adeg_f_pbmax$ANRIND,
-  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
-  labels = c("LOW", "NORMAL", "HIGH", "Missing")
-)
-
-adeg_f_pbmax$BNRIND[sample(1:nrow(adeg_f_pbmax), size = 5)] <- "Missing"
-adeg_f_pbmax$ANRIND[sample(1:nrow(adeg_f_pbmax), size = 5)] <- "Missing"
-
-attr(adeg_f_pbmax$ANRIND, "label") <- "Analysis Reference Range Indicator"
-attr(adeg_f_pbmax$BNRIND, "label") <- "Baseline Reference Range Indicator"
-
-# Temporary solution for overarching column
-adeg_f_pbmax <- adeg_f_pbmax %>% mutate(max_label = "Maximum Post-Baseline Assessment")
-
-# Define the split function
-split_fun <- drop_split_levels
-
-lyt <- basic_table() %>%
-  split_cols_by("max_label") %>%
-  split_cols_by("ANRIND") %>%
-  split_rows_by("ARMCD", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f_pbmax$ARMCD)) %>%
-  add_rowcounts() %>%
-  analyze_vars("BNRIND", denom = "N_row", .stats = "count_fraction") %>%
-  append_varlabels(adeg_f_pbmax, c("BNRIND"), indent = 1L)
-
-result <- build_table(
-  lyt = lyt,
-  df = adeg_f_pbmax
-)
-
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| screenshot.opts = list(delay = 16)
-
-library(dplyr)
-library(tern)
-library(scda)
-library(teal.modules.clinical)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-app <- init(
-  data = cdisc_data(
-    cdisc_dataset("ADSL", adsl, code = 'ADSL <- synthetic_cdisc_dataset("latest", "adsl")'),
-    cdisc_dataset("ADEG", adeg, code = 'ADEG <- synthetic_cdisc_dataset("latest", "adeg")'),
-    check = TRUE
-  ),
-  modules = modules(
-    tm_t_shift_by_arm(
-      label = "Shift by Arm Table",
-      dataname = "ADEG",
-      arm_var = choices_selected(
-        variable_choices(adsl, subset = c("ARM", "ARMCD")),
-        selected = "ARM"
-      ),
-      paramcd = choices_selected(
-        value_choices(adeg, "PARAMCD"),
-        selected = "HR"
-      ),
-      visit_var = choices_selected(
-        value_choices(adeg, "AVISIT"),
-        selected = "POST-BASELINE MINIMUM"
-      ),
-      aval_var = choices_selected(
-        variable_choices(adeg, subset = "ANRIND"),
-        selected = "ANRIND", fixed = TRUE
-      ),
-      base_var = choices_selected(
-        variable_choices(adeg, subset = "BNRIND"),
-        selected = "BNRIND", fixed = TRUE
-      ),
-      treatment_flag_var = choices_selected(
-        variable_choices(adeg, subset = "ONTRTFL"),
-        selected = "ONTRTFL", fixed = TRUE
-      ),
-      treatment_flag = choices_selected(
-        value_choices(adeg, "ONTRTFL"),
-        selected = "Y", fixed = TRUE
-      )
-    )
-  ),
-  filter = list(ADSL = list(SAFFL = "Y"))
-)
-
-shinyApp(app$ui, app$server)
-```
-
-{{< include ../../repro.qmd >}}
-
-:::
diff --git a/book/tables/ECG/egt04.qmd b/book/tables/ECG/egt04.qmd
deleted file mode 100644
index fb960b9b2a..0000000000
--- a/book/tables/ECG/egt04.qmd
+++ /dev/null
@@ -1,100 +0,0 @@
----
-title: EGT04
-subtitle: Shift Table of Qualitative ECG Assessments
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-For the EGT04 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N.
-For illustration purposes, missing data are added to the example dataset.
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(scda)
-library(tern)
-library(dplyr)
-set.seed(123)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adeg <- df_explicit_na(adeg, omit_columns = c("AVALC", "BASEC"))
-
-adeg_labels <- var_labels(adeg)
-
-adeg_f <- subset(
-  adeg,
-  PARAMCD == "ECGINTP" & # Analysis in terms of "NORMAL"/"ABNORMAL" (AVALC)
-    SAFFL == "Y" & # "Safety Population Flag"
-    ONTRTFL == "Y" & # "On Treatment Record Flag"
-    WORS02FL == "Y" # "Worst Post-Baseline Observation"
-)
-
-adeg_f$AVALC[sample(1:nrow(adeg_f), size = 5)] <- "Missing"
-adeg_f$BASEC[sample(1:nrow(adeg_f), size = 5)] <- "Missing"
-adeg_f$AVALC <- factor(
-  adeg_f$AVALC,
-  levels = c("NORMAL", "ABNORMAL", "Missing"),
-  labels = c("Normal", "Abnormal", "Missing")
-)
-adeg_f$BASEC <- factor(
-  adeg_f$BASEC,
-  levels = c("NORMAL", "ABNORMAL", "Missing"),
-  labels = c("Normal", "Abnormal", "Missing")
-)
-
-var_labels(adeg_f) <- adeg_labels
-adeg_f <- adeg_f %>%
-  var_relabel(BASEC = "Baseline")
-
-# Temprary solution for over arching column
-adeg_f <- adeg_f %>% mutate(postbaseline_label = "Post-Baseline")
-```
-
-## Standard Table
-
-The EGT04 template consists of a stacked list of contingency tables, one per group.
-For each group, the n's across all cells must add up to the group N in the analysis, and percentages are calculated using N as the denominator.
-
-```{r variant1, test = list(result_v1 = "result")}
-# Define the split function
-split_fun <- drop_split_levels
-
-lyt <- basic_table() %>%
-  split_cols_by("postbaseline_label") %>%
-  split_cols_by("AVALC") %>%
-  split_rows_by("ARM", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f$ARM)) %>%
-  add_rowcounts() %>%
-  analyze_vars(
-    "BASEC",
-    denom = "N_row",
-    .stats = "count_fraction",
-    na.rm = FALSE
-  ) %>%
-  append_varlabels(adeg_f, c("BASEC"), indent = 1L)
-
-result <- build_table(lyt, adeg_f)
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| code-fold: show
-
-# In progress
-```
-
-{{< include ../../repro.qmd >}}
-
-:::
diff --git a/book/tables/ECG/egt05_qtcat.qmd b/book/tables/ECG/egt05_qtcat.qmd
deleted file mode 100644
index db249bacc5..0000000000
--- a/book/tables/ECG/egt05_qtcat.qmd
+++ /dev/null
@@ -1,255 +0,0 @@
----
-title: EGT05_QTCAT
-subtitle: ECG Actual Values and Changes from Baseline by Visit
----
-
-------------------------------------------------------------------------
-
-{{< include ../../test-utils/envir_hook.qmd >}}
-
-::: panel-tabset
-## Data Setup
-
-```{r setup, message=FALSE}
-#| code-fold: show
-
-library(tern)
-library(scda)
-library(dplyr)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-
-# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
-adsl <- df_explicit_na(adsl)
-adeg <- df_explicit_na(adeg)
-
-adeg_labels <- var_labels(adeg)
-adeg_f <- adeg %>%
-  filter(
-    PARAMCD == "QT",
-    ANL01FL == "Y"
-  ) %>%
-  mutate(
-    AVALCAT1 = case_when(
-      AVAL <= 450 ~ "<=450 msec",
-      AVAL <= 480 ~ ">450 to <=480 msec",
-      AVAL <= 500 ~ ">480 to <= 500 msec",
-      AVAL > 500 ~ ">500 msec",
-      is.na(AVAL) ~ "<Missing>"
-    ),
-    CHGCAT1 = case_when(
-      CHG <= 30 ~ "<=30 msec",
-      CHG <= 60 ~ ">30 to <=60 msec",
-      CHG > 60 ~ ">60 msec",
-      is.na(CHG) ~ "<Missing>"
-    )
-  ) %>%
-  mutate(
-    AVALCAT1 = factor(
-      AVALCAT1,
-      levels = c(
-        "<=450 msec",
-        ">450 to <=480 msec",
-        ">480 to <= 500 msec",
-        ">500 msec",
-        "<Missing>"
-      )
-    ),
-    CHGCAT1 = factor(
-      CHGCAT1,
-      levels = c(
-        "<=30 msec",
-        ">30 to <=60 msec",
-        ">60 msec",
-        "<Missing>"
-      )
-    )
-  ) %>%
-  var_relabel(
-    AVALCAT1 = "Value at Visit",
-    CHGCAT1 = "Change from Baseline"
-  )
-```
-
-## Standard Table
-
-```{r variant1, test = list(result_v1 = "result")}
-split_fun <- drop_split_levels
-
-lyt <- basic_table(show_colcounts = TRUE) %>%
-  split_cols_by("ARM") %>%
-  split_rows_by(
-    "PARAM",
-    split_label = obj_label(adeg_f$PARAM),
-    split_fun = split_fun,
-    label_pos = "topleft"
-  ) %>%
-  split_rows_by(
-    "AVISIT",
-    split_label = obj_label(adeg_f$AVISIT),
-    split_fun = split_fun,
-    label_pos = "topleft"
-  ) %>%
-  analyze_vars(
-    vars = c("AVALCAT1", "CHGCAT1"),
-    var_labels = c("Value at Visit", "Change from Baseline")
-  ) %>%
-  append_topleft("    Category")
-
-result <- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl) %>%
-  prune_table()
-
-result
-```
-
-{{< include ../../test-utils/save_results.qmd >}}
-
-## `teal` App
-
-```{r teal, message=FALSE, opts.label='skip_if_testing'}
-#| screenshot.opts = list(delay = 24)
-
-library(teal.modules.clinical)
-library(scda)
-library(dplyr)
-
-adsl <- synthetic_cdisc_dataset("latest", "adsl")
-adeg <- synthetic_cdisc_dataset("latest", "adeg")
-adsl <- df_explicit_na(adsl)
-adeg <- df_explicit_na(adeg)
-adeg_labels <- var_labels(adeg)
-
-adeg_f <- adeg %>%
-  filter(
-    ANL01FL == "Y" # no need to filter for PARAMCD here
-  ) %>%
-  mutate(
-    AVALCAT1 = case_when(
-      AVAL <= 450 ~ "<=450 msec",
-      AVAL <= 480 ~ ">450 to <=480 msec",
-      AVAL <= 500 ~ ">480 to <= 500 msec",
-      AVAL > 500 ~ ">500 msec",
-      is.na(AVAL) ~ "<Missing>"
-    ),
-    CHGCAT1 = case_when(
-      CHG <= 30 ~ "<=30 msec",
-      CHG <= 60 ~ ">30 to <=60 msec",
-      CHG > 60 ~ ">60 msec",
-      is.na(CHG) ~ "<Missing>"
-    )
-  ) %>%
-  mutate(
-    AVALCAT1 = factor(
-      AVALCAT1,
-      levels = c(
-        "<=450 msec",
-        ">450 to <=480 msec",
-        ">480 to <= 500 msec",
-        ">500 msec",
-        "<Missing>"
-      )
-    ),
-    CHGCAT1 = factor(
-      CHGCAT1,
-      levels = c(
-        "<=30 msec",
-        ">30 to <=60 msec",
-        ">60 msec",
-        "<Missing>"
-      )
-    )
-  )
-var_labels(adeg_f) <- c(
-  adeg_labels,
-  "AVALCAT1" = "Value at Visit",
-  "CHGCAT1" = "Change from Baseline"
-)
-app <- init(
-  data = cdisc_data(
-    cdisc_dataset("ADSL",
-      adsl,
-      code = 'ADSL <- synthetic_cdisc_dataset("latest", "adsl")
-                  ADSL <- df_explicit_na(ADSL)'
-    ),
-    cdisc_dataset("ADEG",
-      adeg_f,
-      code = 'ADEG <- synthetic_cdisc_dataset("latest", "adeg")
-                  ADEG <- df_explicit_na(ADEG)
-                  ADEG_labels <- var_labels(ADEG)
-
-                  ADEG <- ADEG %>%
-                    filter(
-                      ANL01FL == "Y"
-                    ) %>%
-                     mutate(
-                      AVALCAT1 = case_when(
-                        AVAL <= 450 ~ "<=450 msec",
-                        AVAL <= 480 ~ ">450 to <=480 msec",
-                        AVAL <= 500 ~ ">480 to <= 500 msec",
-                        AVAL > 500 ~ ">500 msec",
-                        is.na(AVAL) ~ "<Missing>"),
-                      CHGCAT1 = case_when(
-                        CHG <= 30 ~ "<=30 msec",
-                        CHG <= 60 ~ ">30 to <=60 msec",
-                        CHG > 60 ~ ">60 msec",
-                        is.na(CHG) ~ "<Missing>")
-                    ) %>% mutate(
-                      AVALCAT1 = factor(
-                        AVALCAT1,
-                        levels = c(
-                          "<=450 msec",
-                          ">450 to <=480 msec",
-                          ">480 to <= 500 msec",
-                          ">500 msec",
-                          "<Missing>"
-                        )
-                      ),
-                      CHGCAT1 = factor(
-                        CHGCAT1,
-                        levels = c(
-                          "<=30 msec",
-                          ">30 to <=60 msec",
-                          ">60 msec",
-                          "<Missing>"
-                        )
-                      )
-                    )
-                  var_labels(ADEG) <- c(
-                    ADEG_labels,
-                    "AVALCAT1" = "Value at Visit",
-                    "CHGCAT1" = "Change from Baseline"
-                  )'
-    ),
-    check = TRUE
-  ),
-  modules = modules(
-    tm_t_summary_by(
-      label = "ECG Actual Values and Changes from Baseline by Visit",
-      dataname = "ADEG",
-      arm_var = choices_selected(
-        choices = variable_choices(adsl, c("ARM", "ARMCD")),
-        selected = "ARM"
-      ),
-      by_vars = choices_selected(
-        choices = variable_choices(adeg_f, c("PARAM", "AVISIT")),
-        selected = c("AVISIT")
-      ),
-      summarize_vars = choices_selected(
-        choices = variable_choices(adeg_f, c("AVALCAT1", "CHGCAT1")),
-        selected = c("AVALCAT1", "CHGCAT1")
-      ),
-      useNA = "ifany",
-      paramcd = choices_selected(
-        choices = value_choices(adeg_f, "PARAMCD", "PARAM"),
-        selected = "QT"
-      )
-    )
-  )
-)
-
-shinyApp(app$ui, app$server)
-```
-
-{{< include ../../repro.qmd >}}
-:::

From cdf7d4847b96f9330f1fd5a9db35d30f8face93e Mon Sep 17 00:00:00 2001
From: Emily de la Rua <emily.de_la_rua@contractors.roche.com>
Date: Thu, 21 Sep 2023 13:28:41 -0400
Subject: [PATCH 11/11] Restore deleted files, regenerate index

---
 book/listings/ADA/adal02.qmd    | 131 ++++++++++++++++
 book/listings/ECG/egl01.qmd     |  89 +++++++++++
 book/tables/ADA/adat01.qmd      | 230 ++++++++++++++++++++++++++++
 book/tables/ADA/adat02.qmd      | 151 +++++++++++++++++++
 book/tables/ADA/adat03.qmd      | 131 ++++++++++++++++
 book/tables/ADA/adat04a.qmd     | 240 ++++++++++++++++++++++++++++++
 book/tables/ADA/adat04b.qmd     | 223 ++++++++++++++++++++++++++++
 book/tables/ECG/egt01.qmd       | 142 ++++++++++++++++++
 book/tables/ECG/egt02.qmd       | 126 ++++++++++++++++
 book/tables/ECG/egt03.qmd       | 204 +++++++++++++++++++++++++
 book/tables/ECG/egt04.qmd       | 100 +++++++++++++
 book/tables/ECG/egt05_qtcat.qmd | 255 ++++++++++++++++++++++++++++++++
 book/tlg-index.qmd              |  62 ++++----
 13 files changed, 2053 insertions(+), 31 deletions(-)
 create mode 100644 book/listings/ADA/adal02.qmd
 create mode 100644 book/listings/ECG/egl01.qmd
 create mode 100644 book/tables/ADA/adat01.qmd
 create mode 100644 book/tables/ADA/adat02.qmd
 create mode 100644 book/tables/ADA/adat03.qmd
 create mode 100644 book/tables/ADA/adat04a.qmd
 create mode 100644 book/tables/ADA/adat04b.qmd
 create mode 100644 book/tables/ECG/egt01.qmd
 create mode 100644 book/tables/ECG/egt02.qmd
 create mode 100644 book/tables/ECG/egt03.qmd
 create mode 100644 book/tables/ECG/egt04.qmd
 create mode 100644 book/tables/ECG/egt05_qtcat.qmd

diff --git a/book/listings/ADA/adal02.qmd b/book/listings/ADA/adal02.qmd
new file mode 100644
index 0000000000..9ddcaf7f56
--- /dev/null
+++ b/book/listings/ADA/adal02.qmd
@@ -0,0 +1,131 @@
+---
+title: ADAL02
+subtitle: Listing of Anti-Drug Antibody Data for Treatment Emergent ADA Positive Patients
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(dplyr)
+library(rlistings)
+library(scda)
+
+adab <- synthetic_cdisc_dataset("latest", "adab") %>%
+  filter(NFRLT %% 1 == 0 & NFRLT > 0)
+
+trt <- "A: Drug X"
+drug_a <- "A: Drug X Antibody"
+drugcd <- unique(adab$PARAMCD[adab$PARAM == "Antibody titer units"])[1]
+min_titer <- 1.10
+
+adab_x <- adab %>%
+  filter(
+    ARM == trt,
+    PARCAT1 == drug_a,
+    ADPBLPFL == "Y"
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %>%
+  unique() %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  filter(if_any(matches("Treatment Emergent - Positive"), ~ .x == 1)) %>%
+  # filter(`Treatment Emergent - Positive` == 1) %>%
+  mutate(
+    VISN = factor(paste0(
+      VISIT, "\n(Day ",
+      ifelse(
+        NFRLT %% 1 == 0,
+        NFRLT,
+        as.character(format(round(NFRLT, 2), nsmall = 2))
+      ),
+      ")"
+    )),
+    PTES = ifelse(
+      ifelse("Treatment induced ADA" %in% names(.), `Treatment induced ADA` == 1, F),
+      ifelse(
+        "Transient ADA" %in% names(.) & `Transient ADA` == 1,
+        "Induced (Transient)",
+        "Induced (Persistent)"
+      ),
+      "Enhanced"
+    )
+  ) %>%
+  mutate(
+    AVAL = paste0(
+      ifelse(
+        ifelse("ADA interpreted per sample result" %in% names(.), `ADA interpreted per sample result` == 0, F),
+        "NEGATIVE",
+        ifelse(
+          ifelse("Antibody titer units" %in% names(.), !is.na(`Antibody titer units`), F),
+          ifelse(
+            `Antibody titer units` < min_titer,
+            paste0("<", format(min_titer, nsmall = 2)),
+            as.character(format(round(`Antibody titer units`, 2), nsmall = 2))
+          ),
+          "---"
+        )
+      ),
+      ifelse(
+        ifelse("NAB interpreted per sample result" %in% names(.), `NAB interpreted per sample result` == 1, F),
+        "*",
+        ""
+      )
+    )
+  )
+
+out <- adab_x %>%
+  select(USUBJID, VISN, AVAL, PTES) %>%
+  tidyr::pivot_wider(
+    names_from = VISN,
+    values_from = AVAL
+  ) %>%
+  select(USUBJID, unique(adab_x$VISN[order(adab_x$NFRLT)]), PTES)
+
+var_labels(out) <- names(out)
+
+out <- out %>%
+  var_relabel(
+    USUBJID = "Subject ID",
+    PTES = "Patient Treatment\nEmergent ADA Status"
+  )
+```
+
+## Standard Listing
+
+```{r lsting, test = list(lsting = "lsting")}
+lsting <- as_listing(
+  out,
+  key_cols = "USUBJID",
+  disp_cols = names(out),
+  main_title = paste0(
+    "Listing of Anti-", drugcd, " Antibody Data for Treatment Emergent ADA Positive Patients, PK Population",
+    "\nProtocol: ", unique(adab$PARCAT1)[1]
+  ),
+  subtitles = paste("\nTreatment Group:", trt),
+  main_footer = "Minimum reportable titer = 1.10 (example only)
+--- = No sample evaluated
+ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies)
+Number of patients positive for Treatment Emergent ADA = the number of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.
+Treatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.
+Treatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
+Transient ADA = ADA positive result detected (a) at only one post-baseline sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints during treatment where the first and last ADA positive samples  are separated by a period of < 16 weeks, irrespective of any negative samples in between.
+Persistent ADA =  ADA positive result detected (a) at the last post-baseline sampling timepoint, OR (b) at 2 or more time points during treatment where the first and last ADA positive samples are separated by a period ≥ 16 weeks, irrespective of any negative samples in between.
+Asterisk denotes sample that tested positive for Neutralizing Antibodies."
+)
+
+head(lsting, 20)
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/listings/ECG/egl01.qmd b/book/listings/ECG/egl01.qmd
new file mode 100644
index 0000000000..a595b864c9
--- /dev/null
+++ b/book/listings/ECG/egl01.qmd
@@ -0,0 +1,89 @@
+---
+title: EGL01
+subtitle: 'Listing of ECG Data: Safety-Evaluable Patients'
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(dplyr)
+library(scda)
+library(rlistings)
+
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+get_param_unit_range <- function(dataset) {
+  u_rng <- lapply(unique(dataset$PARAMCD), function(p) {
+    dat <- dataset %>% filter(PARAMCD == p)
+    list(
+      unit = unique(dat$AVALU),
+      range = paste0(unique(dat$ANRLO), "-", unique(dat$ANRHI))
+    )
+  })
+  names(u_rng) <- unique(dataset$PARAMCD)
+  u_rng
+}
+
+eg_u_rng <- get_param_unit_range(adeg)
+
+adeg_sub <- adeg %>%
+  filter(!is.na(AVAL) & SAFFL == "Y" & ANL01FL == "Y" & !is.na(EGSEQ) & PARAMCD != "ECGINTP") %>%
+  mutate(
+    CRTNPT = paste(SITEID, sub("^.*-([[:alnum:]]+)$", "\\1", SUBJID), sep = "/"),
+    AGSXRC = paste(AGE, SEX, RACE, sep = "/"),
+    AVAL = format(round(AVAL, 2), nsmall = 2),
+    AVAL_ANRIND = ifelse(ANRIND %in% c("NORMAL", ""), AVAL, paste(AVAL, substr(ANRIND, 1, 1), sep = "/")),
+    CHG = format(round(CHG, 2), nsmall = 2)
+  )
+
+anl_eg <- adeg_sub %>%
+  select(SUBJID, CRTNPT, AGSXRC, TRT01A, PARAMCD, AVAL_ANRIND, CHG, ADY, AVISIT, ADTM) %>%
+  tidyr::pivot_wider(
+    id_cols = c(SUBJID, CRTNPT, AGSXRC, TRT01A, ADY, AVISIT, ADTM),
+    names_from = PARAMCD,
+    values_from = c(AVAL_ANRIND, CHG)
+  )
+
+out <- anl_eg %>%
+  select(CRTNPT, AGSXRC, TRT01A, AVISIT, ADY, AVAL_ANRIND_HR, CHG_HR, AVAL_ANRIND_QT, CHG_QT, AVAL_ANRIND_RR, CHG_RR) %>%
+  var_relabel(
+    CRTNPT = "Center/Subject ID",
+    AGSXRC = "Age/Sex/Race",
+    TRT01A = "Treatment",
+    AVISIT = "Visit",
+    ADY = "Study\nDay",
+    AVAL_ANRIND_HR = paste0("Heart Rate Result\n(", eg_u_rng$HR$unit, ");\nRange:(", eg_u_rng$HR$range, ")"),
+    CHG_HR = "Heart Rate\nChange from BL",
+    AVAL_ANRIND_QT = paste0("QT Duration Result\n(", eg_u_rng$QT$unit, ");\nRange:(", eg_u_rng$QT$range, ")"),
+    CHG_QT = "QT Duration\nChange from BL",
+    AVAL_ANRIND_RR = paste0("RR Duration Result\n(", eg_u_rng$RR$unit, ");\nRange:(", eg_u_rng$RR$range, ")"),
+    CHG_RR = "RR Duration\nChange from BL"
+  )
+```
+
+## Standard Listing
+
+```{r lsting, test = list(lsting = "lsting")}
+lsting <- as_listing(
+  out,
+  key_cols = c("CRTNPT", "AGSXRC", "TRT01A", "AVISIT", "ADY"),
+  disp_cols = names(out),
+  main_title = "Listing of ECG Data: Safety-Evaluable Patients",
+  main_footer = "Baseline is the patient's last observation prior to initiation of study drug. Abnormalities are flagged as high (H) or low (L) if
+  outside the Roche standard reference range."
+)
+
+head(lsting, 20)
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ADA/adat01.qmd b/book/tables/ADA/adat01.qmd
new file mode 100644
index 0000000000..31a13bd819
--- /dev/null
+++ b/book/tables/ADA/adat01.qmd
@@ -0,0 +1,230 @@
+---
+title: ADAT01
+subtitle: Baseline Prevalence and Incidence of Treatment Emergent ADA
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(tern)
+library(dplyr)
+library(scda)
+library(tibble)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adab <- synthetic_cdisc_dataset("latest", "adab")
+
+combodf <- tribble(
+  ~valname, ~label, ~levelcombo, ~exargs,
+  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
+  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
+)
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adab <- adab %>%
+  filter(PARCAT1 == "A: Drug X Antibody") %>%
+  select(-ARRLT, -NRRLT)
+
+# Baseline Pts
+adab_b <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL == "Y",
+    ADABLPFL == "Y",
+    PARAM %in% c("ADA interpreted per sample result")
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU) %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  mutate_at(
+    c("ADA interpreted per sample result"),
+    as.logical
+  ) %>%
+  mutate(
+    ADABLPFL = ADABLPFL == "Y",
+    PADABLPFL = `ADA interpreted per sample result` == "TRUE",
+    NADABLPFL = `ADA interpreted per sample result` == "FALSE"
+  ) %>%
+  var_relabel(
+    ADABLPFL = "Baseline evaluable patients",
+    PADABLPFL = "Patient with a positive sample at baseline",
+    NADABLPFL = "Patient with no positive samples at baseline"
+  )
+
+# Post Baseline Treatment Enhanced NAb positive Pts
+adab_pb <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL != "Y",
+    ADPBLPFL == "Y",
+    PARAM %in% c(
+      "ADA interpreted per sample result",
+      "Treatment Emergent - Positive",
+      "Treatment induced ADA",
+      "Treatment enhanced ADA",
+      "Treatment Emergent - Negative",
+      "Treatment unaffected"
+    )
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU) %>%
+  unique() %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  mutate(
+    across(
+      any_of(c(
+        "ADA interpreted per sample result", "Treatment Emergent - Positive",
+        "Treatment induced ADA", "Treatment enhanced ADA",
+        "Treatment Emergent - Negative", "Treatment unaffected"
+      )),
+      as.logical
+    )
+  ) %>%
+  mutate(
+    ADPBLPFL = ADPBLPFL == "Y",
+    PTEFL = if (exists("Treatment Emergent - Positive", where = .)) `Treatment Emergent - Positive` == "TRUE" else FALSE,
+    TIFL = if (exists("Treatment induced ADA", where = .)) `Treatment induced ADA` == "TRUE" else FALSE,
+    TEFL = if (exists("Treatment enhanced ADA", where = .)) `Treatment enhanced ADA` == "TRUE" else FALSE,
+    NTEFL = if (exists("Treatment Emergent - Negative", where = .)) `Treatment Emergent - Negative` == "TRUE" else FALSE,
+    TUFL = if (exists("Treatment unaffected", where = .)) `Treatment unaffected` == "TRUE" else FALSE
+  ) %>%
+  var_relabel(
+    ADPBLPFL = "Post-baseline evaluable patients",
+    PTEFL = "Patient positive for Treatment Emergent ADA",
+    TIFL = "Treatment-induced ADA",
+    TEFL = "Treatment-enhanced ADA",
+    NTEFL = "Patient negative for Treatment Emergent ADA",
+    TUFL = "Treatment unaffected"
+  )
+```
+
+## Standard Table
+
+```{r variant1, test = list(result_v1 = "result")}
+# Layout for Baseline Prevalence of NAbs
+lyt_bl <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = add_combo_levels(combodf)
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "ADABLPFL",
+    .stats = "count",
+    var_labels = "Baseline Prevalence of ADAs",
+    show_labels = "visible",
+    table_names = "t1"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "PADABLPFL",
+    table_names = "t2",
+    .indent_mods = 1L,
+    var_labels = "a",
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NADABLPFL",
+    .stats = "count",
+    show_labels = "hidden",
+    .indent_mods = 1L,
+    table_names = "t3"
+  )
+
+# Layout for incidence of NAbs
+lyt_pb <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = add_combo_levels(combodf)
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "ADPBLPFL",
+    .stats = "count",
+    var_labels = "Incidence of Treatment Emergent ADAs",
+    show_labels = "visible",
+    table_names = "tb1"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "PTEFL",
+    table_names = "tb2",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("TIFL", "TEFL"),
+    .stats = "count",
+    table_names = "tb3",
+    .indent_mods = 2L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NTEFL",
+    table_names = "tb4",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "TUFL",
+    .stats = "count",
+    table_names = "tb5",
+    .indent_mods = 2L,
+    show_labels = "hidden"
+  )
+
+result_1 <- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)
+result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
+
+# Combine tables.
+result_1@col_info <- result_2@col_info
+result <- rbind(result_1, result_2)
+
+# Change the column order.
+result <- cbind_rtables(result[, 1], result[, 3]) %>%
+  cbind_rtables(result[, 4]) %>%
+  cbind_rtables(result[, 2]) %>%
+  cbind_rtables(result[, 5])
+
+main_title(result) <- paste(
+  "Baseline Prevalence and Incidence of Treatment Emergent ADA"
+)
+main_footer(result) <- paste(
+  "ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient = a patient with an ADA assay result from a baseline sample(s)
+  Post-baseline evaluable patient = a patient with an ADA assay result from at least one post-baseline sample Number of patients positive for Treatment Emergent
+  ADA = the number (and percentage) of post-baseline evaluable patients determined to have treatment-induced ADA or treatment-enhanced ADA during the study period.
+  Treatment-induced ADA = a patient with negative or missing baseline ADA result(s) and at least one positive post-baseline ADA result.
+  Treatment-enhanced ADA = a patient with positive ADA result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
+  Number of patients negative for Treatment Emergent ADA = number of post-baseline evaluable patients with negative or missing baseline ADA result(s) and all negative post-baseline results, or a patient who is treatment unaffected.
+  Treatment unaffected = A post-baseline evaluable patient with a positive ADA result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing.
+  For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer."
+)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| code-fold: show
+
+# In progress
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ADA/adat02.qmd b/book/tables/ADA/adat02.qmd
new file mode 100644
index 0000000000..630e8e817e
--- /dev/null
+++ b/book/tables/ADA/adat02.qmd
@@ -0,0 +1,151 @@
+---
+title: ADAT02
+subtitle: Summary of Patients with Treatment-Induced ADA
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(tern)
+library(dplyr)
+library(scda)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adab <- synthetic_cdisc_dataset("latest", "adab")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adab <- df_explicit_na(adab) %>%
+  mutate(ADPBLPFL = "Y") %>% # temp fix
+  filter(
+    ADPBLPFL == "Y",
+    !PARAM %in% c(
+      "NAB interpreted per sample result",
+      "NAB Status of a patient",
+      "Treatment enhanced ADA"
+    )
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %>%
+  unique() %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  mutate(
+    across(
+      any_of(c(
+        "Treatment induced ADA",
+        "Transient ADA",
+        "Persistent ADA"
+      )),
+      as.logical
+    )
+  ) %>%
+  mutate(
+    ADPBLPFL = ADPBLPFL == "Y",
+    TI_ADA = if (exists("Treatment induced ADA", where = .)) `Treatment induced ADA` else FALSE
+  ) %>%
+  var_relabel(
+    ADPBLPFL = "Post-baseline evaluable patients",
+    TI_ADA = "Treatment-induced ADA patients"
+  )
+
+adab_ti <- adab %>%
+  filter(TI_ADA)
+```
+
+## Standard Table
+
+```{r variant1, test = list(result_v1 = "result")}
+# Layout for post-baseline evaluable patient variables from adab dataset.
+lyt_adab <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = drop_split_levels
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "ADPBLPFL",
+    .stats = "count",
+    table_names = "post_baseline"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "TI_ADA"
+  )
+
+# Layout for treatment-induced patient variables from adab dataset.
+lyt_adab_ti <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = keep_split_levels(c("A: Drug X", "C: Combination", "<Missing>")) # temp fix
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("Transient ADA", "Persistent ADA"),
+    var_labels = "Treatment-induced ADA patients with",
+    show_labels = "visible"
+  ) %>%
+  analyze_vars(
+    "Time to onset of ADA",
+    .stats = "median",
+    nested = FALSE,
+    .labels = c(median = "Median time to onset of ADA (weeks)")
+  ) %>%
+  analyze_vars(
+    "Antibody titer units",
+    .stats = "range",
+    nested = FALSE,
+    .labels = c(range = "ADA titer range (min - max)")
+  )
+
+result_adab <- build_table(lyt_adab, df = adab, alt_counts_df = adsl)
+result_adab_ti <- build_table(lyt_adab_ti, df = adab_ti, alt_counts_df = adsl)
+
+# Combine tables.
+col_info(result_adab) <- col_info(result_adab_ti)
+result <- rbind(
+  result_adab,
+  result_adab_ti
+)
+
+main_title(result) <- paste(
+  "Summary of Patients with Treatment-Induced ADA, PK Population"
+)
+subtitles(result) <- paste("Protocol:", unique(adab$PARCAT1)[1])
+main_footer(result) <- paste(
+  "ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic
+  Antibodies)
+  Treatment-induced ADA = negative or missing baseline.
+  ADA result(s) and at least one positive post-baseline ADA result.
+  Transient ADA = ADA positive result detected (a) at only one post-baseline
+  sampling timepoint (excluding last timepoint) OR (b) at 2 or more timepoints
+  during treatment where the first and last ADA positive samples are separated
+  by a period of < 16 weeks, irrespective of any negative samples in between.
+  Persistent ADA = ADA positive result detected (a) at the last post-baseline
+  sampling timepoint, OR (b) at 2 or more time points during treatment where
+  the first and last ADA positive samples are separated by a period ≥ 16
+  weeks, irrespective of any negative samples in between."
+)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| code-fold: show
+
+# In progress
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ADA/adat03.qmd b/book/tables/ADA/adat03.qmd
new file mode 100644
index 0000000000..561c5ab1af
--- /dev/null
+++ b/book/tables/ADA/adat03.qmd
@@ -0,0 +1,131 @@
+---
+title: ADAT03
+subtitle: Summary of Serum Concentrations at Timepoints Where ADA Samples Were Collected and Analyzed
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(dplyr)
+library(scda)
+library(tern)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adab <- synthetic_cdisc_dataset("latest", "adab")
+adpc <- synthetic_cdisc_dataset("latest", "adpc")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adab <- df_explicit_na(adab)
+adpc <- df_explicit_na(adpc)
+
+# Adjust zzz parameter
+max_conc <- 15
+
+adpc <- adpc %>% select(USUBJID, NFRLT, AVAL, AVALU, AVALCAT1)
+
+anl <- adab %>%
+  filter(., PARAM == "ADA interpreted per sample result") %>%
+  select(-AVAL, AVALC, AVALU)
+
+anl <- merge(anl, adpc, by = c("USUBJID", "NFRLT")) %>%
+  mutate(AVAL_LT = ifelse(AVAL <= max_conc, TRUE, FALSE))
+```
+
+## Standard Table (μg/mL)
+
+```{r variant1, test = list(result_v1 = "result")}
+# parameters in columns
+adat03_stats <- c("n", "mean", "sd", "median", "min", "max", "cv", "geom_mean", "count_fraction")
+adat03_lbls <- c(
+  n = "Total Number\nof Measurable\n Samples",
+  mean = "Mean",
+  sd = "SD",
+  median = "Median",
+  min = "Minimum",
+  max = "Maximum",
+  cv = "CV (%)",
+  geom_mean = "Geometric Mean",
+  count_fraction = paste0("Samples with\nConcentration\n≤ ", max_conc, "μg/mL")
+)
+adat03_fmts <- c(
+  n = "xx.",
+  mean = sprintf_format("%.3e"),
+  sd = sprintf_format("%.3e"),
+  median = sprintf_format("%.3e"),
+  min = sprintf_format("%.3e"),
+  max = sprintf_format("%.3e"),
+  cv = "xx.x",
+  geom_mean = sprintf_format("%.3e"),
+  count_fraction = format_count_fraction
+)
+
+afun_list <- lapply(
+  1:9,
+  function(i) make_afun(s_summary, .stats = adat03_stats[i], .formats = adat03_fmts[i], .labels = "Overall")
+)
+
+# lyt creation
+lyt <- basic_table() %>%
+  split_rows_by(
+    var = "ARM",
+    label_pos = "topleft",
+    split_label = "Treatment Group",
+    split_fun = drop_split_levels,
+    section_div = ""
+  ) %>%
+  add_rowcounts() %>%
+  split_rows_by(
+    var = "VISIT",
+    label_pos = "topleft",
+    split_label = "Visit",
+    split_fun = drop_split_levels,
+    child_labels = "hidden"
+  ) %>%
+  analyze_vars_in_cols(
+    vars = c(rep("AVAL", 8), "AVAL_LT"),
+    .stats = adat03_stats,
+    .labels = adat03_lbls,
+    .formats = adat03_fmts
+  ) %>%
+  analyze_colvars(
+    afun_list,
+    nested = FALSE,
+    extra_args = list(".labels" = "Overall")
+  )
+
+result <- build_table(lyt, anl, alt_counts_df = adsl)
+
+main_title(result) <- paste(
+  "Summary of Serum Concentrations (μg/mL) at Timepoints Where ADA Samples Were Collected and Analyzed\n
+  Protocol:", unique(adab$PARCAT1)[1]
+)
+subtitles(result) <- paste("Analyte:", unique(adab$PARAMCD)[1])
+fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL")) <- "Refers to the total no. of measurable ADA samples that have a corresponding measurable drug concentration sample (i.e. results with
+valid numeric values and LTRs). LTR results on post-dose samples are replaced by aaa µg/mL i.e. half of MQC value."
+fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL_LT")) <- "In validation, the assay was able to detect yyy ng/mL of surrogate ADA in the presence of zzz µg/mL of [drug]. BLQ = Below Limit of
+Quantitation, LTR = Lower than Reportable, MQC = Minimum Quantifiable Concentration, ADA = Anti-Drug Antibodies (is also referred to as ATA,
+or Anti-Therapeutic Antibodies). RXXXXXXX is also known as [drug]"
+
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| code-fold: show
+
+# In progress
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ADA/adat04a.qmd b/book/tables/ADA/adat04a.qmd
new file mode 100644
index 0000000000..3a3f56d998
--- /dev/null
+++ b/book/tables/ADA/adat04a.qmd
@@ -0,0 +1,240 @@
+---
+title: ADAT04A
+subtitle: Baseline Prevalence and Incidence of Treatment Emergent NAbs
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(tern)
+library(dplyr)
+library(scda)
+library(tibble)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adab <- synthetic_cdisc_dataset("latest", "adab")
+
+combodf <- tribble(
+  ~valname, ~label, ~levelcombo, ~exargs,
+  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
+  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
+)
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+
+# Baseline Pts
+adab_b <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL == "Y",
+    ADABLPFL == "Y",
+    PARAM %in% c(
+      "ADA interpreted per sample result",
+      "NAB interpreted per sample result"
+    )
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU) %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  mutate(
+    across(
+      any_of(c(
+        "ADA interpreted per sample result",
+        "NAB interpreted per sample result"
+      )),
+      as.logical
+    )
+  ) %>%
+  mutate(
+    ADABLPFL = ADABLPFL == "Y",
+    PADABLPFL = if (exists("ADA interpreted per sample result", where = .)) `ADA interpreted per sample result` == "TRUE" else FALSE,
+    PNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "TRUE" else FALSE,
+    NNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "FALSE" else FALSE
+  ) %>%
+  var_relabel(
+    ADABLPFL = "Baseline evaluable patients for ADA",
+    PADABLPFL = "Patients with a positive ADA sample at baseline",
+    PNABBLFL = "Patients with a positive NAb sample at baseline",
+    NNABBLFL = "Patient with no positive NAb samples at baseline"
+  )
+
+# Post Baseline Treatment Enhanced NAb positive Pts
+adab_pb <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL != "Y",
+    ADPBLPFL == "Y",
+    PARAM %in% c(
+      "ADA interpreted per sample result",
+      "Treatment Emergent - Positive, Neutralizing Antibody",
+      "Treatment induced ADA, Neutralizing Antibody",
+      "Treatment enhanced ADA, Neutralizing Antibody",
+      "NAB interpreted per sample result",
+      "Treatment unaffected, Neutralizing Antibody"
+    )
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %>%
+  unique() %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  mutate(
+    across(
+      any_of(c(
+        "ADA interpreted per sample result", "NAB interpreted per sample result",
+        "Treatment Emergent - Positive, Neutralizing Antibody",
+        "Treatment induced ADA, Neutralizing Antibody",
+        "Treatment enhanced ADA, Neutralizing Antibody",
+        "Treatment unaffected, Neutralizing Antibody"
+      )),
+      as.logical
+    )
+  ) %>%
+  mutate(
+    ADPBLPFL = ADPBLPFL == "Y",
+    ADAPFL = if (exists("ADA interpreted per sample result", where = .)) `ADA interpreted per sample result` == "TRUE" else FALSE,
+    TENABPFL = if (exists("Treatment Emergent - Positive, Neutralizing Antibody", where = .)) `Treatment Emergent - Positive, Neutralizing Antibody` == "TRUE" else FALSE,
+    TINPBFL = if (exists("Treatment induced ADA, Neutralizing Antibody", where = .)) `Treatment induced ADA, Neutralizing Antibody` == "TRUE" else FALSE,
+    TENPBFL = if (exists("Treatment enhanced ADA, Neutralizing Antibody", where = .)) `Treatment enhanced ADA, Neutralizing Antibody` == "TRUE" else FALSE,
+    NABNFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "FALSE" else FALSE,
+    TUNPBFL = if (exists("Treatment unaffected, Neutralizing Antibody", where = .)) `Treatment unaffected, Neutralizing Antibody` == "TRUE" else FALSE
+  ) %>%
+  var_relabel(
+    ADPBLPFL = "Post-baseline evaluable patients for ADA",
+    ADAPFL = "Patients positive for ADA",
+    TENABPFL = "Patients positive for Treatment Emergent NAb",
+    TINPBFL = "Treatment-induced NAb",
+    TENPBFL = "Treatment-enhanced NAb",
+    NABNFL = "Patients negative for NAb",
+    TUNPBFL = "Treatment unaffected"
+  )
+```
+
+## Summary of Treatment Emergent NAbs
+
+```{r variant1, test = list(result_v1 = "result")}
+# Layout for Baseline Prevalence of NAbs
+lyt_bl <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = add_combo_levels(combodf)
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("ADABLPFL", "PADABLPFL"),
+    table_names = "t1",
+    .stats = "count",
+    var_labels = "Baseline Prevalence of NAbs",
+    show_labels = "visible"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "PNABBLFL",
+    table_names = "t2",
+    .indent_mods = 1L
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NNABBLFL",
+    .stats = "count",
+    table_names = "t3",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  )
+
+# Layout for incidence of NAbs
+lyt_pb <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = add_combo_levels(combodf)
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("ADPBLPFL", "ADAPFL"),
+    .stats = "count",
+    var_labels = "Incidence of Treatment Emergent NAbs",
+    show_labels = "visible",
+    table_names = "tb1"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "TENABPFL",
+    table_names = "tb2",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("TINPBFL", "TENPBFL"),
+    .stats = "count",
+    table_names = "tb3",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NABNFL",
+    table_names = "tb4",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "TUNPBFL",
+    .stats = "count",
+    table_names = "tb5",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  )
+
+result_1 <- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)
+result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
+
+# Combine tables
+result <- rbind(result_1, result_2)
+
+# Change the column order
+result <- cbind_rtables(result[, 1], result[, 3]) %>%
+  cbind_rtables(result[, 4]) %>%
+  cbind_rtables(result[, 2]) %>%
+  cbind_rtables(result[, 5])
+
+main_title(result) <- paste(
+  "Baseline Prevalence and Incidence of Treatment Emergent NAbs"
+)
+subtitles(result) <- paste("Protocol:", unique(adab$PARCAT1)[1])
+main_footer(result) <- paste(
+  "NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s)
+  Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s)
+  Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample
+  Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample
+  Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.
+Number of patients positive for Treatment Emergent NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have treatment-induced NAb or treatment-enhanced NAb during the study period.
+Treatment-induced = a patient with negative or missing baseline result(s) and at least one positive post-baseline result. Treatment-enhanced = a patient with positive result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
+Number of patients negative for Treatment Emergent NAb = number of post-baseline evaluable patients with negative or missing baseline NAb result(s) and all negative post-baseline NAb results, or a patient who is NAb treatment unaffected.
+Treatment unaffected = A post-baseline evaluable patient with a positive result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing. For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer."
+)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| code-fold: show
+
+# In progress
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ADA/adat04b.qmd b/book/tables/ADA/adat04b.qmd
new file mode 100644
index 0000000000..5994f04002
--- /dev/null
+++ b/book/tables/ADA/adat04b.qmd
@@ -0,0 +1,223 @@
+---
+title: ADAT04B
+subtitle: Baseline Prevalence and Incidence of NAbs
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(tern)
+library(dplyr)
+library(scda)
+library(tibble)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adab <- synthetic_cdisc_dataset("latest", "adab")
+
+combodf <- tribble(
+  ~valname, ~label, ~levelcombo, ~exargs,
+  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
+  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
+)
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+
+# Baseline Pts
+adab_b <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL == "Y",
+    ADABLPFL == "Y",
+    PARAM %in% c(
+      "ADA interpreted per sample result",
+      "NAB interpreted per sample result"
+    )
+  ) %>%
+  select(-PARAMCD, -AVALC, -AVALU) %>%
+  tidyr::pivot_wider(
+    names_from = PARAM,
+    values_from = AVAL
+  ) %>%
+  mutate(
+    across(
+      any_of(c(
+        "ADA interpreted per sample result",
+        "NAB interpreted per sample result"
+      )),
+      as.logical
+    )
+  ) %>%
+  mutate(
+    ADABLPFL = ADABLPFL == "Y",
+    PADABLPFL = if (exists("ADA interpreted per sample result", where = .)) `ADA interpreted per sample result` == "TRUE" else FALSE,
+    PNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "TRUE" else FALSE,
+    NNABBLFL = if (exists("NAB interpreted per sample result", where = .)) `NAB interpreted per sample result` == "FALSE" else FALSE
+  ) %>%
+  var_relabel(
+    ADABLPFL = "Baseline evaluable patients for ADA",
+    PADABLPFL = "Patients with a positive ADA sample at baseline",
+    PNABBLFL = "Patients with a positive NAb sample at baseline",
+    NNABBLFL = "Patients with no positive NAb sample at baseline"
+  )
+
+# Post-Baseline Evaluable Pts
+adab_pb_ada <- df_explicit_na(adab) %>%
+  filter(ADPBLPFL == "Y") %>%
+  select(STUDYID, USUBJID, ARM, ACTARM, ADPBLPFL) %>%
+  mutate(ADPBLPFL = ADPBLPFL == "Y") %>%
+  distinct()
+
+# Post-Baseline ADA Positive Pts
+adab_pb_adap <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL != "Y",
+    PARAM == "ADA interpreted per sample result",
+    AVALC == "POSITIVE"
+  ) %>%
+  mutate(ADAPFL = AVALC == "POSITIVE") %>%
+  select(STUDYID, USUBJID, ARM, ACTARM, ADAPFL) %>%
+  distinct()
+
+# Post-Baseline NAb Positive Pts
+adab_pb_nabp <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL != "Y",
+    PARAM == "NAb interpreted per sample result",
+    AVALC == "POSITIVE"
+  ) %>%
+  mutate(NABPFL = AVALC == "POSITIVE") %>%
+  select(STUDYID, USUBJID, ARM, ACTARM, NABPFL) %>%
+  distinct()
+
+# Post-Baseline NAb Negative Pts
+adab_pb_nabn <- df_explicit_na(adab) %>%
+  filter(
+    ABLFL != "Y",
+    PARAM == "NAb interpreted per sample result",
+    AVALC == "NEGATIVE"
+  ) %>%
+  rename(NABNFL = AVALC) %>%
+  select(STUDYID, USUBJID, ARM, ACTARM, NABNFL) %>%
+  distinct()
+
+mergecol <- c("STUDYID", "USUBJID", "ARM", "ACTARM")
+
+adab_pb <- left_join(adab_pb_ada, adab_pb_adap, by = mergecol) %>%
+  left_join(adab_pb_nabp, by = mergecol) %>%
+  mutate(
+    NABNFL = ifelse(is.na(NABPFL), "TRUE", "FALSE"),
+    NABNFL = NABNFL == "TRUE"
+  ) %>%
+  var_relabel(
+    ADPBLPFL = "Post-baseline evaluable patients for ADA",
+    ADAPFL = "Patients positive for ADA",
+    NABPFL = "Patients positive for NAb",
+    NABNFL = "Patients negative for NAb"
+  )
+```
+
+## Standard Table
+
+```{r variant1, test = list(result_v1 = "result")}
+# Layout for Baseline Prevalence of NAbs
+lyt_bl <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = add_combo_levels(combodf)
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("ADABLPFL", "PADABLPFL"),
+    .stats = "count",
+    var_labels = "Baseline Prevalence of NAbs",
+    show_labels = "visible",
+    table_names = "t1"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "PNABBLFL",
+    table_names = "t2",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NNABBLFL",
+    .stats = "count",
+    table_names = "t3",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  )
+
+# Layout for incidence of NAbs
+lyt_pb <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(
+    "ACTARM",
+    split_fun = add_combo_levels(combodf)
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = c("ADPBLPFL", "ADAPFL"),
+    .stats = "count",
+    var_labels = "Incidence of NAbs",
+    show_labels = "visible",
+    table_names = "tb1"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NABPFL",
+    table_names = "tb2",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  ) %>%
+  count_patients_with_flags(
+    "USUBJID",
+    flag_variables = "NABNFL",
+    .stats = "count",
+    table_names = "tb3",
+    .indent_mods = 1L,
+    show_labels = "hidden"
+  )
+
+result_1 <- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)
+result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
+
+# Combine tables.
+result <- rbind(result_1, result_2)
+
+# Change the column order.
+result <- cbind_rtables(result[, 1], result[, 3]) %>%
+  cbind_rtables(result[, 4]) %>%
+  cbind_rtables(result[, 2]) %>%
+  cbind_rtables(result[, 5])
+
+main_title(result) <- paste(
+  "Baseline Prevalence and Incidence of Neutralizing Antibodies (NAbs)"
+)
+subtitles(result) <- paste("Protocol:", unique(adab$PARCAT1)[1])
+main_footer(result) <- paste(
+  "NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s) Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s) Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.
+Number of patients positive for NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have at least one positive post-baseline NAb result during the study period. Number of patients negative for NAb = number of post-baseline evaluable patients with all negative post-baseline NAb results."
+)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| code-fold: show
+
+# In progress
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ECG/egt01.qmd b/book/tables/ECG/egt01.qmd
new file mode 100644
index 0000000000..017188e635
--- /dev/null
+++ b/book/tables/ECG/egt01.qmd
@@ -0,0 +1,142 @@
+---
+title: EGT01
+subtitle: ECG Results and Change from Baseline by Visit
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(dplyr)
+library(tern)
+library(scda)
+
+# Data should be filtered for the studied Parameter (`PARAM`) and the
+# Analysis Visit (`AVISIT`). According to the GDSR template, the values for
+# the `AVISIT` reported in the EGT01 standard may be:
+# 'POST-BASELINE MAXIMUM', 'POST-BASELINE MINIMUM', 'POST-BASELINE LAST'.
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adeg <- df_explicit_na(adeg)
+
+adeg_f <- adeg %>%
+  filter(ANL01FL == "Y") %>%
+  filter(PARAM %in% c("Heart Rate", "QT Duration", "RR Duration"))
+```
+
+## Standard Table
+
+```{r variant1, test = list(result_v1 = "result")}
+# Define the split function
+split_fun <- drop_split_levels
+
+afun <- function(x, .var, .spl_context, ...) {
+  n_fun <- sum(!is.na(x), na.rm = TRUE)
+  if (n_fun == 0) {
+    mean_sd_fun <- c(NA, NA)
+    median_fun <- NA
+    min_max_fun <- c(NA, NA)
+  } else {
+    mean_sd_fun <- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))
+    median_fun <- median(x, na.rm = TRUE)
+    min_max_fun <- c(min(x), max(x))
+  }
+  is_chg <- .var == "CHG"
+  is_baseline <- .spl_context$value[which(.spl_context$split == "AVISIT")] == "BASELINE"
+  if (is_baseline && is_chg) n_fun <- mean_sd_fun <- median_fun <- min_max_fun <- NULL
+
+  in_rows(
+    "n" = n_fun,
+    "Mean (SD)" = mean_sd_fun,
+    "Median" = median_fun,
+    "Min - Max" = min_max_fun,
+    .formats = list("n" = "xx", "Mean (SD)" = "xx.xx (xx.xx)", "Median" = "xx.xx", "Min - Max" = "xx.xx - xx.xx"),
+    .format_na_strs = list("n" = "NE", "Mean (SD)" = "NE (NE)", "Median" = "NE", "Min - Max" = "NE - NE")
+  )
+}
+
+lyt <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by("ACTARM") %>%
+  split_rows_by("PARAM", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f$PARAM)) %>%
+  split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f$AVISIT)) %>%
+  split_cols_by_multivar(
+    vars = c("AVAL", "CHG"),
+    varlabels = c("Value at Visit", "Change from\nBaseline")
+  ) %>%
+  analyze_colvars(afun = afun)
+
+result <- build_table(lyt, adeg_f, alt_counts_df = adsl)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| screenshot.opts = list(delay = 16)
+
+library(teal.modules.clinical)
+library(scda)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adeg <- df_explicit_na(adeg)
+
+app <- init(
+  data = cdisc_data(
+    cdisc_dataset("ADSL",
+      adsl,
+      code = 'ADSL <- df_explicit_na(synthetic_cdisc_dataset("latest", "adsl"))'
+    ),
+    cdisc_dataset("ADEG",
+      adeg,
+      code = 'ADEG <- df_explicit_na(synthetic_cdisc_dataset("latest", "adeg"))'
+    ),
+    check = TRUE
+  ),
+  modules = modules(
+    tm_t_summary_by(
+      label = "ECG Results and Change from Baseline by Visit",
+      dataname = "ADEG",
+      arm_var = choices_selected(
+        choices = variable_choices(adsl, c("ARM", "ARMCD")),
+        selected = "ARM"
+      ),
+      by_vars = choices_selected(
+        choices = variable_choices(adeg, c("PARAM", "AVISIT")),
+        selected = c("AVISIT")
+      ),
+      summarize_vars = choices_selected(
+        choices = variable_choices(adeg, c("AVAL", "CHG")),
+        selected = c("AVAL")
+      ),
+      useNA = "ifany",
+      paramcd = choices_selected(
+        choices = value_choices(adeg, "PARAMCD", "PARAM"),
+        selected = "HR"
+      ),
+      parallel_vars = TRUE
+    )
+  ),
+  filter = list(ADEG = list(AVAL = list()))
+)
+
+shinyApp(app$ui, app$server)
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ECG/egt02.qmd b/book/tables/ECG/egt02.qmd
new file mode 100644
index 0000000000..addbda506f
--- /dev/null
+++ b/book/tables/ECG/egt02.qmd
@@ -0,0 +1,126 @@
+---
+title: EGT02
+subtitle: ECG Abnormalities (EGT02_1 & EGT02_2)
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(dplyr)
+library(scda)
+library(tern)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adeg <- df_explicit_na(adeg)
+
+# Note:  We keep only post-baseline for analysis.
+adeg_f <- adeg %>%
+  filter(ONTRTFL == "Y") %>%
+  filter(PARAM %in% c("Heart Rate", "QT Duration", "RR Duration")) %>%
+  filter(ANRIND != "<Missing>") %>%
+  var_relabel(
+    PARAM = "Assessment",
+    ANRIND = "Abnormality"
+  )
+```
+
+## ECG Abnormalities Regardless <br/> of Abnormality at Baseline
+
+```{r variant1, test = list(result_v1 = "result")}
+split_fun <- drop_split_levels
+
+lyt <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(var = "ACTARM") %>%
+  split_rows_by(
+    "PARAM",
+    split_fun = split_fun,
+    label_pos = "topleft",
+    split_label = obj_label(adeg_f$PARAM)
+  ) %>%
+  count_abnormal("ANRIND", abnormal = list(Low = "LOW", High = "HIGH"), exclude_base_abn = FALSE) %>%
+  append_varlabels(adeg_f, "ANRIND", indent = 1L)
+
+result <- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl)
+result
+```
+
+## ECG Abnormalities Among Subjects <br/> Without Abnormality at Baseline
+
+```{r variant2, test = list(result_v2 = "result")}
+split_fun <- drop_split_levels
+
+lyt <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by(var = "ACTARM") %>%
+  split_rows_by(
+    "PARAM",
+    split_fun = split_fun,
+    label_pos = "topleft",
+    split_label = obj_label(adeg_f$PARAM)
+  ) %>%
+  count_abnormal("ANRIND", abnormal = list(Low = "LOW", High = "HIGH"), exclude_base_abn = TRUE) %>%
+  append_varlabels(adeg_f, "ANRIND", indent = 1L)
+
+result <- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| screenshot.opts = list(delay = 18)
+
+library(scda)
+library(teal.modules.clinical)
+library(dplyr)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+app <- init(
+  data = cdisc_data(
+    cdisc_dataset("ADSL", adsl, code = 'ADSL <- synthetic_cdisc_dataset("latest", "adsl")'),
+    cdisc_dataset("ADEG", adeg, code = 'ADEG <- synthetic_cdisc_dataset("latest", "adeg")'),
+    check = TRUE
+  ),
+  modules = modules(
+    tm_t_abnormality(
+      label = "Abnormality Table",
+      dataname = "ADEG",
+      arm_var = choices_selected(
+        choices = variable_choices(adsl, subset = c("ARM", "ARMCD")),
+        selected = "ARM"
+      ),
+      by_vars = choices_selected(
+        choices = variable_choices(adeg, subset = c("EGCAT", "PARAM", "AVISIT")),
+        selected = c("PARAM"),
+        keep_order = TRUE
+      ),
+      grade = choices_selected(
+        choices = variable_choices(adeg, subset = "ANRIND"),
+        selected = "ANRIND",
+        fixed = TRUE
+      ),
+      abnormal = list(Low = "LOW", High = "HIGH"),
+      exclude_base_abn = FALSE
+    )
+  )
+)
+
+shinyApp(app$ui, app$server)
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tables/ECG/egt03.qmd b/book/tables/ECG/egt03.qmd
new file mode 100644
index 0000000000..6d335eb12e
--- /dev/null
+++ b/book/tables/ECG/egt03.qmd
@@ -0,0 +1,204 @@
+---
+title: EGT03
+subtitle: Shift Table of ECG Interval Data -- Baseline Versus Minimum/Maximum Post-Baseline
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(scda)
+library(tern)
+library(dplyr)
+
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adeg <- df_explicit_na(adeg)
+
+adeg_labels <- var_labels(adeg)
+
+adeg_f_pbmin <- subset(
+  adeg,
+  PARAMCD == "HR" & # Heart Rate
+    SAFFL == "Y" & # "Safety Population Flag"
+    ONTRTFL == "Y" & # "On Treatment Record Flag"
+    AVISIT == "POST-BASELINE MINIMUM" # "Analysis Visit"
+)
+
+adeg_f_pbmax <- subset(
+  adeg,
+  PARAMCD == "HR" & # Heart Rate
+    SAFFL == "Y" & # "Safety Population Flag"
+    ONTRTFL == "Y" & # "On Treatment Record Flag"
+    AVISIT == "POST-BASELINE MAXIMUM" # "Analysis Visit"
+)
+
+var_labels(adeg_f_pbmin) <- adeg_labels
+var_labels(adeg_f_pbmax) <- adeg_labels
+```
+
+## Table of Baseline Versus <br/> Minimum Post-Baseline
+
+For the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N.
+For illustration purposes, missing data are added to the example dataset.
+
+```{r variant1, test = list(result_v1 = "result")}
+set.seed(123, kind = "Mersenne-Twister")
+
+adeg_f_pbmin$BNRIND <- factor(
+  adeg_f_pbmin$BNRIND,
+  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
+  labels = c("LOW", "NORMAL", "HIGH", "Missing")
+)
+adeg_f_pbmin$ANRIND <- factor(
+  adeg_f_pbmin$ANRIND,
+  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
+  labels = c("LOW", "NORMAL", "HIGH", "Missing")
+)
+
+adeg_f_pbmin$BNRIND[sample(1:nrow(adeg_f_pbmin), size = 5)] <- "Missing"
+adeg_f_pbmin$ANRIND[sample(1:nrow(adeg_f_pbmin), size = 5)] <- "Missing"
+
+attr(adeg_f_pbmin$ANRIND, "label") <- "Analysis Reference Range Indicator"
+attr(adeg_f_pbmin$BNRIND, "label") <- "Baseline Reference Range Indicator"
+
+# Temporary solution for overarching column
+adeg_f_pbmin <- adeg_f_pbmin %>% mutate(min_label = "Minimum Post-Baseline Assessment")
+
+# Define the split function
+split_fun <- drop_split_levels
+
+lyt <- basic_table() %>%
+  split_cols_by("min_label") %>%
+  split_cols_by("ANRIND") %>%
+  split_rows_by("ARMCD", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f_pbmin$ARMCD)) %>%
+  add_rowcounts() %>%
+  analyze_vars("BNRIND", denom = "N_row", .stats = "count_fraction") %>%
+  append_varlabels(adeg_f_pbmin, c("BNRIND"), indent = 1L)
+
+result <- build_table(
+  lyt = lyt,
+  df = adeg_f_pbmin
+)
+
+result
+```
+
+## Table of Baseline Versus <br/> Maximum Post-Baseline
+
+For the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N.
+For illustration purpose, missing data are added to the example dataset.
+
+```{r variant2, test = list(result_v2 = "result")}
+set.seed(123, kind = "Mersenne-Twister")
+
+adeg_f_pbmax$BNRIND <- factor(
+  adeg_f_pbmax$BNRIND,
+  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
+  labels = c("LOW", "NORMAL", "HIGH", "Missing")
+)
+adeg_f_pbmax$ANRIND <- factor(
+  adeg_f_pbmax$ANRIND,
+  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
+  labels = c("LOW", "NORMAL", "HIGH", "Missing")
+)
+
+adeg_f_pbmax$BNRIND[sample(1:nrow(adeg_f_pbmax), size = 5)] <- "Missing"
+adeg_f_pbmax$ANRIND[sample(1:nrow(adeg_f_pbmax), size = 5)] <- "Missing"
+
+attr(adeg_f_pbmax$ANRIND, "label") <- "Analysis Reference Range Indicator"
+attr(adeg_f_pbmax$BNRIND, "label") <- "Baseline Reference Range Indicator"
+
+# Temporary solution for overarching column
+adeg_f_pbmax <- adeg_f_pbmax %>% mutate(max_label = "Maximum Post-Baseline Assessment")
+
+# Define the split function
+split_fun <- drop_split_levels
+
+lyt <- basic_table() %>%
+  split_cols_by("max_label") %>%
+  split_cols_by("ANRIND") %>%
+  split_rows_by("ARMCD", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f_pbmax$ARMCD)) %>%
+  add_rowcounts() %>%
+  analyze_vars("BNRIND", denom = "N_row", .stats = "count_fraction") %>%
+  append_varlabels(adeg_f_pbmax, c("BNRIND"), indent = 1L)
+
+result <- build_table(
+  lyt = lyt,
+  df = adeg_f_pbmax
+)
+
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| screenshot.opts = list(delay = 16)
+
+library(dplyr)
+library(tern)
+library(scda)
+library(teal.modules.clinical)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+app <- init(
+  data = cdisc_data(
+    cdisc_dataset("ADSL", adsl, code = 'ADSL <- synthetic_cdisc_dataset("latest", "adsl")'),
+    cdisc_dataset("ADEG", adeg, code = 'ADEG <- synthetic_cdisc_dataset("latest", "adeg")'),
+    check = TRUE
+  ),
+  modules = modules(
+    tm_t_shift_by_arm(
+      label = "Shift by Arm Table",
+      dataname = "ADEG",
+      arm_var = choices_selected(
+        variable_choices(adsl, subset = c("ARM", "ARMCD")),
+        selected = "ARM"
+      ),
+      paramcd = choices_selected(
+        value_choices(adeg, "PARAMCD"),
+        selected = "HR"
+      ),
+      visit_var = choices_selected(
+        value_choices(adeg, "AVISIT"),
+        selected = "POST-BASELINE MINIMUM"
+      ),
+      aval_var = choices_selected(
+        variable_choices(adeg, subset = "ANRIND"),
+        selected = "ANRIND", fixed = TRUE
+      ),
+      base_var = choices_selected(
+        variable_choices(adeg, subset = "BNRIND"),
+        selected = "BNRIND", fixed = TRUE
+      ),
+      treatment_flag_var = choices_selected(
+        variable_choices(adeg, subset = "ONTRTFL"),
+        selected = "ONTRTFL", fixed = TRUE
+      ),
+      treatment_flag = choices_selected(
+        value_choices(adeg, "ONTRTFL"),
+        selected = "Y", fixed = TRUE
+      )
+    )
+  ),
+  filter = list(ADSL = list(SAFFL = "Y"))
+)
+
+shinyApp(app$ui, app$server)
+```
+
+{{< include ../../repro.qmd >}}
+
+:::
diff --git a/book/tables/ECG/egt04.qmd b/book/tables/ECG/egt04.qmd
new file mode 100644
index 0000000000..fb960b9b2a
--- /dev/null
+++ b/book/tables/ECG/egt04.qmd
@@ -0,0 +1,100 @@
+---
+title: EGT04
+subtitle: Shift Table of Qualitative ECG Assessments
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+For the EGT04 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N.
+For illustration purposes, missing data are added to the example dataset.
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(scda)
+library(tern)
+library(dplyr)
+set.seed(123)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adeg <- df_explicit_na(adeg, omit_columns = c("AVALC", "BASEC"))
+
+adeg_labels <- var_labels(adeg)
+
+adeg_f <- subset(
+  adeg,
+  PARAMCD == "ECGINTP" & # Analysis in terms of "NORMAL"/"ABNORMAL" (AVALC)
+    SAFFL == "Y" & # "Safety Population Flag"
+    ONTRTFL == "Y" & # "On Treatment Record Flag"
+    WORS02FL == "Y" # "Worst Post-Baseline Observation"
+)
+
+adeg_f$AVALC[sample(1:nrow(adeg_f), size = 5)] <- "Missing"
+adeg_f$BASEC[sample(1:nrow(adeg_f), size = 5)] <- "Missing"
+adeg_f$AVALC <- factor(
+  adeg_f$AVALC,
+  levels = c("NORMAL", "ABNORMAL", "Missing"),
+  labels = c("Normal", "Abnormal", "Missing")
+)
+adeg_f$BASEC <- factor(
+  adeg_f$BASEC,
+  levels = c("NORMAL", "ABNORMAL", "Missing"),
+  labels = c("Normal", "Abnormal", "Missing")
+)
+
+var_labels(adeg_f) <- adeg_labels
+adeg_f <- adeg_f %>%
+  var_relabel(BASEC = "Baseline")
+
+# Temprary solution for over arching column
+adeg_f <- adeg_f %>% mutate(postbaseline_label = "Post-Baseline")
+```
+
+## Standard Table
+
+The EGT04 template consists of a stacked list of contingency tables, one per group.
+For each group, the n's across all cells must add up to the group N in the analysis, and percentages are calculated using N as the denominator.
+
+```{r variant1, test = list(result_v1 = "result")}
+# Define the split function
+split_fun <- drop_split_levels
+
+lyt <- basic_table() %>%
+  split_cols_by("postbaseline_label") %>%
+  split_cols_by("AVALC") %>%
+  split_rows_by("ARM", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f$ARM)) %>%
+  add_rowcounts() %>%
+  analyze_vars(
+    "BASEC",
+    denom = "N_row",
+    .stats = "count_fraction",
+    na.rm = FALSE
+  ) %>%
+  append_varlabels(adeg_f, c("BASEC"), indent = 1L)
+
+result <- build_table(lyt, adeg_f)
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| code-fold: show
+
+# In progress
+```
+
+{{< include ../../repro.qmd >}}
+
+:::
diff --git a/book/tables/ECG/egt05_qtcat.qmd b/book/tables/ECG/egt05_qtcat.qmd
new file mode 100644
index 0000000000..db249bacc5
--- /dev/null
+++ b/book/tables/ECG/egt05_qtcat.qmd
@@ -0,0 +1,255 @@
+---
+title: EGT05_QTCAT
+subtitle: ECG Actual Values and Changes from Baseline by Visit
+---
+
+------------------------------------------------------------------------
+
+{{< include ../../test-utils/envir_hook.qmd >}}
+
+::: panel-tabset
+## Data Setup
+
+```{r setup, message=FALSE}
+#| code-fold: show
+
+library(tern)
+library(scda)
+library(dplyr)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+
+# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
+adsl <- df_explicit_na(adsl)
+adeg <- df_explicit_na(adeg)
+
+adeg_labels <- var_labels(adeg)
+adeg_f <- adeg %>%
+  filter(
+    PARAMCD == "QT",
+    ANL01FL == "Y"
+  ) %>%
+  mutate(
+    AVALCAT1 = case_when(
+      AVAL <= 450 ~ "<=450 msec",
+      AVAL <= 480 ~ ">450 to <=480 msec",
+      AVAL <= 500 ~ ">480 to <= 500 msec",
+      AVAL > 500 ~ ">500 msec",
+      is.na(AVAL) ~ "<Missing>"
+    ),
+    CHGCAT1 = case_when(
+      CHG <= 30 ~ "<=30 msec",
+      CHG <= 60 ~ ">30 to <=60 msec",
+      CHG > 60 ~ ">60 msec",
+      is.na(CHG) ~ "<Missing>"
+    )
+  ) %>%
+  mutate(
+    AVALCAT1 = factor(
+      AVALCAT1,
+      levels = c(
+        "<=450 msec",
+        ">450 to <=480 msec",
+        ">480 to <= 500 msec",
+        ">500 msec",
+        "<Missing>"
+      )
+    ),
+    CHGCAT1 = factor(
+      CHGCAT1,
+      levels = c(
+        "<=30 msec",
+        ">30 to <=60 msec",
+        ">60 msec",
+        "<Missing>"
+      )
+    )
+  ) %>%
+  var_relabel(
+    AVALCAT1 = "Value at Visit",
+    CHGCAT1 = "Change from Baseline"
+  )
+```
+
+## Standard Table
+
+```{r variant1, test = list(result_v1 = "result")}
+split_fun <- drop_split_levels
+
+lyt <- basic_table(show_colcounts = TRUE) %>%
+  split_cols_by("ARM") %>%
+  split_rows_by(
+    "PARAM",
+    split_label = obj_label(adeg_f$PARAM),
+    split_fun = split_fun,
+    label_pos = "topleft"
+  ) %>%
+  split_rows_by(
+    "AVISIT",
+    split_label = obj_label(adeg_f$AVISIT),
+    split_fun = split_fun,
+    label_pos = "topleft"
+  ) %>%
+  analyze_vars(
+    vars = c("AVALCAT1", "CHGCAT1"),
+    var_labels = c("Value at Visit", "Change from Baseline")
+  ) %>%
+  append_topleft("    Category")
+
+result <- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl) %>%
+  prune_table()
+
+result
+```
+
+{{< include ../../test-utils/save_results.qmd >}}
+
+## `teal` App
+
+```{r teal, message=FALSE, opts.label='skip_if_testing'}
+#| screenshot.opts = list(delay = 24)
+
+library(teal.modules.clinical)
+library(scda)
+library(dplyr)
+
+adsl <- synthetic_cdisc_dataset("latest", "adsl")
+adeg <- synthetic_cdisc_dataset("latest", "adeg")
+adsl <- df_explicit_na(adsl)
+adeg <- df_explicit_na(adeg)
+adeg_labels <- var_labels(adeg)
+
+adeg_f <- adeg %>%
+  filter(
+    ANL01FL == "Y" # no need to filter for PARAMCD here
+  ) %>%
+  mutate(
+    AVALCAT1 = case_when(
+      AVAL <= 450 ~ "<=450 msec",
+      AVAL <= 480 ~ ">450 to <=480 msec",
+      AVAL <= 500 ~ ">480 to <= 500 msec",
+      AVAL > 500 ~ ">500 msec",
+      is.na(AVAL) ~ "<Missing>"
+    ),
+    CHGCAT1 = case_when(
+      CHG <= 30 ~ "<=30 msec",
+      CHG <= 60 ~ ">30 to <=60 msec",
+      CHG > 60 ~ ">60 msec",
+      is.na(CHG) ~ "<Missing>"
+    )
+  ) %>%
+  mutate(
+    AVALCAT1 = factor(
+      AVALCAT1,
+      levels = c(
+        "<=450 msec",
+        ">450 to <=480 msec",
+        ">480 to <= 500 msec",
+        ">500 msec",
+        "<Missing>"
+      )
+    ),
+    CHGCAT1 = factor(
+      CHGCAT1,
+      levels = c(
+        "<=30 msec",
+        ">30 to <=60 msec",
+        ">60 msec",
+        "<Missing>"
+      )
+    )
+  )
+var_labels(adeg_f) <- c(
+  adeg_labels,
+  "AVALCAT1" = "Value at Visit",
+  "CHGCAT1" = "Change from Baseline"
+)
+app <- init(
+  data = cdisc_data(
+    cdisc_dataset("ADSL",
+      adsl,
+      code = 'ADSL <- synthetic_cdisc_dataset("latest", "adsl")
+                  ADSL <- df_explicit_na(ADSL)'
+    ),
+    cdisc_dataset("ADEG",
+      adeg_f,
+      code = 'ADEG <- synthetic_cdisc_dataset("latest", "adeg")
+                  ADEG <- df_explicit_na(ADEG)
+                  ADEG_labels <- var_labels(ADEG)
+
+                  ADEG <- ADEG %>%
+                    filter(
+                      ANL01FL == "Y"
+                    ) %>%
+                     mutate(
+                      AVALCAT1 = case_when(
+                        AVAL <= 450 ~ "<=450 msec",
+                        AVAL <= 480 ~ ">450 to <=480 msec",
+                        AVAL <= 500 ~ ">480 to <= 500 msec",
+                        AVAL > 500 ~ ">500 msec",
+                        is.na(AVAL) ~ "<Missing>"),
+                      CHGCAT1 = case_when(
+                        CHG <= 30 ~ "<=30 msec",
+                        CHG <= 60 ~ ">30 to <=60 msec",
+                        CHG > 60 ~ ">60 msec",
+                        is.na(CHG) ~ "<Missing>")
+                    ) %>% mutate(
+                      AVALCAT1 = factor(
+                        AVALCAT1,
+                        levels = c(
+                          "<=450 msec",
+                          ">450 to <=480 msec",
+                          ">480 to <= 500 msec",
+                          ">500 msec",
+                          "<Missing>"
+                        )
+                      ),
+                      CHGCAT1 = factor(
+                        CHGCAT1,
+                        levels = c(
+                          "<=30 msec",
+                          ">30 to <=60 msec",
+                          ">60 msec",
+                          "<Missing>"
+                        )
+                      )
+                    )
+                  var_labels(ADEG) <- c(
+                    ADEG_labels,
+                    "AVALCAT1" = "Value at Visit",
+                    "CHGCAT1" = "Change from Baseline"
+                  )'
+    ),
+    check = TRUE
+  ),
+  modules = modules(
+    tm_t_summary_by(
+      label = "ECG Actual Values and Changes from Baseline by Visit",
+      dataname = "ADEG",
+      arm_var = choices_selected(
+        choices = variable_choices(adsl, c("ARM", "ARMCD")),
+        selected = "ARM"
+      ),
+      by_vars = choices_selected(
+        choices = variable_choices(adeg_f, c("PARAM", "AVISIT")),
+        selected = c("AVISIT")
+      ),
+      summarize_vars = choices_selected(
+        choices = variable_choices(adeg_f, c("AVALCAT1", "CHGCAT1")),
+        selected = c("AVALCAT1", "CHGCAT1")
+      ),
+      useNA = "ifany",
+      paramcd = choices_selected(
+        choices = value_choices(adeg_f, "PARAMCD", "PARAM"),
+        selected = "QT"
+      )
+    )
+  )
+)
+
+shinyApp(app$ui, app$server)
+```
+
+{{< include ../../repro.qmd >}}
+:::
diff --git a/book/tlg-index.qmd b/book/tlg-index.qmd
index 19a704936b..8d0efa650c 100644
--- a/book/tlg-index.qmd
+++ b/book/tlg-index.qmd
@@ -10,47 +10,47 @@ toc-depth: 4
 
 #### ADA
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT01 -- Baseline Prevalence and Incidence of Treatment Emergent ADA](./tables/ada/adat01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT01 -- Baseline Prevalence and Incidence of Treatment Emergent ADA](./tables/ADA/adat01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT02 -- Summary of Patients with Treatment-Induced ADA](./tables/ada/adat02.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT02 -- Summary of Patients with Treatment-Induced ADA](./tables/ADA/adat02.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT03 -- Summary of Serum Concentrations at Timepoints Where ADA Samples Were Collected and Analyzed](./tables/ada/adat03.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT03 -- Summary of Serum Concentrations at Timepoints Where ADA Samples Were Collected and Analyzed](./tables/ADA/adat03.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT04A -- Baseline Prevalence and Incidence of Treatment Emergent NAbs](./tables/ada/adat04a.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT04A -- Baseline Prevalence and Incidence of Treatment Emergent NAbs](./tables/ADA/adat04a.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT04B -- Baseline Prevalence and Incidence of NAbs](./tables/ada/adat04b.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAT04B -- Baseline Prevalence and Incidence of NAbs](./tables/ADA/adat04b.qmd)
 
 
 #### Adverse Events
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET01_AESI -- Safety Summary (Adverse Events of Special Interest)](./tables/adverse-events/aet01_aesi.qmd)
-
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET01 -- Safety Summary](./tables/adverse-events/aet01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET02_SMQ -- Adverse Events by Standardized MedDRA Query](./tables/adverse-events/aet02_smq.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET01_AESI -- Safety Summary (Adverse Events of Special Interest)](./tables/adverse-events/aet01_aesi.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET02 -- Adverse Events](./tables/adverse-events/aet02.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET03 -- Adverse Events by Greatest Intensity](./tables/adverse-events/aet03.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET02_SMQ -- Adverse Events by Standardized MedDRA Query](./tables/adverse-events/aet02_smq.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET04_PI -- Adverse Events Reported in $\geq$ 10% of Patients by Highest NCI CTCAE Grade](./tables/adverse-events/aet04_pi.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET03 -- Adverse Events by Greatest Intensity](./tables/adverse-events/aet03.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET04 -- Adverse Events by Highest NCI CTCAE Grade](./tables/adverse-events/aet04.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET05_ALL -- Adverse Event Rate Adjusted for Patient-Years at Risk -- All Occurrences](./tables/adverse-events/aet05_all.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET04_PI -- Adverse Events Reported in $\geq$ 10% of Patients by Highest NCI CTCAE Grade](./tables/adverse-events/aet04_pi.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET05 -- Adverse Event Rate Adjusted for Patient-Years at Risk -- First Occurrence](./tables/adverse-events/aet05.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET06_SMQ -- Adverse Events by Baseline Characteristic, by SMQ and Preferred Term](./tables/adverse-events/aet06_smq.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET05_ALL -- Adverse Event Rate Adjusted for Patient-Years at Risk -- All Occurrences](./tables/adverse-events/aet05_all.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET06 -- Adverse Events by Baseline Characteristic](./tables/adverse-events/aet06.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET07 -- Adverse Events Resulting in Death](./tables/adverse-events/aet07.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET06_SMQ -- Adverse Events by Baseline Characteristic, by SMQ and Preferred Term](./tables/adverse-events/aet06_smq.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET09_SMQ -- Adverse Events Related to Study Drug by Standardized MedDRA Query](./tables/adverse-events/aet09_smq.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET07 -- Adverse Events Resulting in Death](./tables/adverse-events/aet07.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET09 -- Adverse Events Related to Study Drug](./tables/adverse-events/aet09.qmd)
 
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET09_SMQ -- Adverse Events Related to Study Drug by Standardized MedDRA Query](./tables/adverse-events/aet09_smq.qmd)
+
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AET10 -- Most Common ($\geq$ 5%) Adverse Events](./tables/adverse-events/aet10.qmd)
 
 
@@ -95,15 +95,15 @@ toc-depth: 4
 
 #### ECG
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT01 -- ECG Results and Change from Baseline by Visit](./tables/ecg/egt01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT01 -- ECG Results and Change from Baseline by Visit](./tables/ECG/egt01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT02 -- ECG Abnormalities (EGT02_1 & EGT02_2)](./tables/ecg/egt02.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT02 -- ECG Abnormalities (EGT02_1 & EGT02_2)](./tables/ECG/egt02.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT03 -- Shift Table of ECG Interval Data -- Baseline Versus Minimum/Maximum Post-Baseline](./tables/ecg/egt03.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT03 -- Shift Table of ECG Interval Data -- Baseline Versus Minimum/Maximum Post-Baseline](./tables/ECG/egt03.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT04 -- Shift Table of Qualitative ECG Assessments](./tables/ecg/egt04.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT04 -- Shift Table of Qualitative ECG Assessments](./tables/ECG/egt04.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT05_QTCAT -- ECG Actual Values and Changes from Baseline by Visit](./tables/ecg/egt05_qtcat.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGT05_QTCAT -- ECG Actual Values and Changes from Baseline by Visit](./tables/ECG/egt05_qtcat.qmd)
 
 
 #### Efficacy
@@ -164,18 +164,18 @@ toc-depth: 4
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT09 -- Liver Laboratory Tests -- Patients with Elevated Post-Baseline AST or ALT Levels](./tables/lab-results/lbt09.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT10_BL -- Liver Laboratory Tests -- Patients with Elevated Post-Baseline AST or ALT Levels at Two Consecutive Visits (with Respect to Baseline)](./tables/lab-results/lbt10_bl.qmd)
-
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT10 -- Liver Laboratory Tests -- Patients with Elevated Post-Baseline AST or ALT Levels at Two Consecutive Visits (with Respect to ULN)](./tables/lab-results/lbt10.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT11_BL -- Time to First Increase in Liver Laboratory Test Result Meeting Hy's Law Laboratory Critieria (with Respect to Baseline)](./tables/lab-results/lbt11_bl.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT10_BL -- Liver Laboratory Tests -- Patients with Elevated Post-Baseline AST or ALT Levels at Two Consecutive Visits (with Respect to Baseline)](./tables/lab-results/lbt10_bl.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT11 -- Time to First Increase in Liver Laboratory Test Result Meeting Hy's Law Laboratory Critieria (with Respect to ULN)](./tables/lab-results/lbt11.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT12_BL -- Liver Laboratory Tests by Time on Treatment -- Patients with Elevated Post-Baseline AST or ALT Levels (with Respect to Baseline)](./tables/lab-results/lbt12_bl.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT11_BL -- Time to First Increase in Liver Laboratory Test Result Meeting Hy's Law Laboratory Critieria (with Respect to Baseline)](./tables/lab-results/lbt11_bl.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT12 -- Liver Laboratory Tests by Time on Treatment -- Patients with Elevated Post-Baseline AST or ALT Levels (with Respect to ULN)](./tables/lab-results/lbt12.qmd)
 
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT12_BL -- Liver Laboratory Tests by Time on Treatment -- Patients with Elevated Post-Baseline AST or ALT Levels (with Respect to Baseline)](./tables/lab-results/lbt12_bl.qmd)
+
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT13 -- NCI CTCAE Grade Laboratory Abnormalities by Visit and Baseline Grade](./tables/lab-results/lbt13.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBT14 -- Laboratory Test Results Shift Table -- Highest NCI CTCAE Grade Post-Baseline by Baseline NCI CTCAE Grade](./tables/lab-results/lbt14.qmd)
@@ -240,19 +240,19 @@ toc-depth: 4
 
 #### ADA
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAL02 -- Listing of Anti-Drug Antibody Data for Treatment Emergent ADA Positive Patients](./listings/ada/adal02.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[ADAL02 -- Listing of Anti-Drug Antibody Data for Treatment Emergent ADA Positive Patients](./listings/ADA/adal02.qmd)
 
 
 #### Adverse Events
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL01_NOLLT -- Listing of Preferred Terms and Investigator-Specified Adverse Event Terms](./listings/adverse-events/ael01_nollt.qmd)
-
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL01 -- Listing of Preferred Terms, Lowest Level Terms, and Investigator-Specified Adverse Event Terms](./listings/adverse-events/ael01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL02_ED -- Listing of Adverse Events (for Early Development Studies)](./listings/adverse-events/ael02_ed.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL01_NOLLT -- Listing of Preferred Terms and Investigator-Specified Adverse Event Terms](./listings/adverse-events/ael01_nollt.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL02 -- Listing of Adverse Events](./listings/adverse-events/ael02.qmd)
 
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL02_ED -- Listing of Adverse Events (for Early Development Studies)](./listings/adverse-events/ael02_ed.qmd)
+
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL03 -- Listing of Serious Adverse Events](./listings/adverse-events/ael03.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[AEL04 -- Listing of Patient Deaths](./listings/adverse-events/ael04.qmd)
@@ -281,7 +281,7 @@ toc-depth: 4
 
 #### ECG
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGL01 -- 'Listing of ECG Data: Safety-Evaluable Patients'](./listings/ecg/egl01.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[EGL01 -- 'Listing of ECG Data: Safety-Evaluable Patients'](./listings/ECG/egl01.qmd)
 
 
 #### Exposure
@@ -291,14 +291,14 @@ toc-depth: 4
 
 #### Lab Results
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL01_RLS -- Listing of Laboratory Test Results Using Roche Safety Lab Standardization](./listings/lab-results/lbl01_rls.qmd)
-
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL01 -- Listing of Laboratory Test Results](./listings/lab-results/lbl01.qmd)
 
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL02A_RLS -- Listing of Laboratory Abnormalities Defined by Roche Safety Lab Standardization](./listings/lab-results/lbl02a_rls.qmd)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL01_RLS -- Listing of Laboratory Test Results Using Roche Safety Lab Standardization](./listings/lab-results/lbl01_rls.qmd)
 
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL02A -- Listing of Laboratory Abnormalities (constant units)](./listings/lab-results/lbl02a.qmd)
 
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL02A_RLS -- Listing of Laboratory Abnormalities Defined by Roche Safety Lab Standardization](./listings/lab-results/lbl02a_rls.qmd)
+
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[LBL02B -- Listing of Laboratory Abnormalities (variable units)](./listings/lab-results/lbl02b.qmd)