CRAN release documentation update

NEWS.md

@@ -1,33 +1,59 @@
+# sdtm.oak 0.1
+
+The V0.1 release of {sdtm.oak} users can create the majority of the SDTM domains. Domains that are *NOT* in scope for the V0.1 release are DM, Trial Design Domains, SV, SE, RELREC, Associated Person domains, and EPOCH Variable across all domains.
+
+-   Functions for commonly used SDTM mapping Algorithms
+    -   assign_no_ct\
+    -   assign_ct\
+    -   hardcode_no_ct\
+    -   hardcode_ct\
+    -   assign_datetime\
+    -   condition_add\
+-  Functions for SDTM derived variables\
+    -   Derive Baseline Flag or Last Observation Before Exposure Flag\
+    -   Derive the sequence number (--SEQ) variable\
+    -   Derive study day\
+    -   ISO8601 date, datetime conversion.\
+-  Functions to support {sdtm.oak}
+    - derive oak id variables
+    - Create conditioned dataframes to support if then else conditions in SDTM mappings
+    - Import study controlled terminology
+-  Articles
+    - Algorithms
+    - Creating an Events SDTM domain
+    - Creating a Findings SDTM domain
+    - Conditioned Data Frames
+    - Converting dates, times or date-times to ISO 8601
+    - Path to Automation
+
 # sdtm.oak 0.0.0.9005 (development version)
 
-* New function for creating conditioned data frames: `condition_add()`.
-* New pipe operator: `%.>%` for explicit dot placeholder placement.
-* `oak_id_vars()` is now an exported function.
+-   New function for creating conditioned data frames: `condition_add()`.
+-   New pipe operator: `%.>%` for explicit dot placeholder placement.
+-   `oak_id_vars()` is now an exported function.
 
 # sdtm.oak 0.0.0.9004 (development version)
 
-* New function: `derive_seq()` for deriving a sequence number variable.
+-   New function: `derive_seq()` for deriving a sequence number variable.
 
 # sdtm.oak 0.0.0.9003 (development version)
 
 ## New Features
 
-* New function: `assign_datetime()` for deriving an ISO8601 date-time variable.
+-   New function: `assign_datetime()` for deriving an ISO8601 date-time variable.
 
 # sdtm.oak 0.0.0.9002 (development version)
 
 ## New Features
 
-* New function: `derive_study_day()` for study day calculation.
+-   New function: `derive_study_day()` for study day calculation.
 
-* New functions for basic SDTM derivations: ` assign_no_ct()`, `assign_ct()`,
-`hardcode_no_ct()` and `hardcode_ct()`.
+-   New functions for basic SDTM derivations: `assign_no_ct()`, `assign_ct()`, `hardcode_no_ct()` and `hardcode_ct()`.
 
-* New functions for handling controlled terminologies: `read_ct_spec()`,
-`read_ct_spec_example()`, `ct_spec_example()` and `ct_map()`.
+-   New functions for handling controlled terminologies: `read_ct_spec()`, `read_ct_spec_example()`, `ct_spec_example()` and `ct_map()`.
 
 # sdtm.oak 0.0.0.9001 (development version)
 
 ## New Features
 
-* New function `create_iso8601()` for conversion of vectors of dates, times or date-times to ISO8601 format.
+-   New function `create_iso8601()` for conversion of vectors of dates, times or date-times to ISO8601 format.

README.Rmd

@@ -26,9 +26,62 @@ can automate SDTM creation based on the standard SDTM spec.
 
 ## Installation
 
+The package is available from CRAN and can be installed with:
+
+```r
+install.packages("sdtm.oak")
+```
+
 You can install the development version of `{sdtm.oak}` from [GitHub](https://github.com/pharmaverse/sdtm.oak/) with:
 
 ``` r
 # install.packages("remotes")
 remotes::install_github("pharmaverse/sdtm.oak")
 ```
+
+## Challenges with SDTM at the Industry Level
+
+* Raw Data Structure: Data from different EDC systems come in varying structures, with different variable names, dataset names, etc.
+
+* Varying Data Collection Standards: Despite the availability of CDASH, pharmaceutical companies still create different eCRFs using CDASH standards.
+
+Due to the differences in raw data structures and data collection standards, it may seem impossible to develop a common approach for programming SDTM datasets.
+
+## GOAL
+`{sdtm.oak}` aims to address this issue by providing an EDC-agnostic, standards-agnostic solution. It is an open-source R package that offers a framework for the modular programming of SDTM in R. With future releases; it will also strive to automate the creation of SDTM datasets based on the metadata-driven approach using standard SDTM specifications.
+
+## Scope
+Our goal is to use `{sdtm.oak}` to program most of the domains specified in SDTMIG (Study Data Tabulation Model Implementation Guide: Human Clinical Trials) and SDTMIG-AP (Study Data Tabulation Model Implementation Guide: Associated Persons). This R package is based on the core concept of `algorithms`, implemented as functions capable of carrying out the SDTM mappings for any domains listed in the CDISC SDTMIG and across different versions of SDTM IGs. The design of these functions allows users to specify a raw dataset and a variable name(s) as parameters, making it EDC (Electronic Data Capture) agnostic. As long as the raw dataset and variable name(s) exist, `{sdtm.oak}` will execute the SDTM mapping using the selected function. It's important to note that `{sdtm.oak}` may not handle sponsor-specific details related to managing metadata for LAB tests, unit conversions, and coding information, as many companies have unique business processes. With subsequent releases, strive to automate SDTM creation using a metadata-driven approach based on a standard SDTM specification format.
+
+## Road Map
+
+This Release: The V0.1 release of `{sdtm.oak}` users can create the majority of the SDTM domains. Domains that are NOT in scope for the V0.1 release are DM, Trial Design Domains, SV, SE, RELREC, Associated Person domains, and EPOCH Variable across all domains.
+
+Subsequent Releases:
+We are planning to develop the below features in the subsequent releases.\
+- Functions required to derive reference date variables in the DM domain.\
+- Metadata driven automation based on the standardized SDTM specification.\
+- Functions required to program the EPOCH Variable.\
+- Functions to derive standard units and results based on metadata.
+
+## References and Documentation
+
+* Please go to [Algorithms](https://pharmaverse.github.io/sdtm.oak/articles/algorithms.html) article to learn about Algorithms.
+* Please go to [Create Events Domain](https://pharmaverse.github.io/sdtm.oak/articles/events_domain.html) to learn about step by step process to create an Events domain.
+* Please go to [Create Findings Domain](https://pharmaverse.github.io/sdtm.oak/articles/findings_domain.html) to learn about step by step process to create an Events domain.
+
+## Feedback
+
+We ask users to follow the mentioned approach and try `{sdtm.oak}` to map any SDTM domains supported in this release. Users can also utilize the test data in the package to become familiar with the concepts before attempting on their own data. Please get in touch with us using one of the recommended approaches listed below:
+
+- [Slack](https://oakgarden.slack.com/)
+- [GitHub](https://github.com/pharmaverse/sdtm.oak/issues)
+
+## Acknowledgments
+
+We thank the contributors and authors of the package. We also thank the CDISC COSA for sponsoring the `{sdtm.oak}`. Additionally, we would like to sincerely thank the volunteers from Roche, Pfizer, GSK, Vertex, and Merck for their valuable input as integral members of the CDISC COSA - OAK leadership team.
+
+
+
+
+

README.md

@@ -17,10 +17,104 @@ standard SDTM spec.
 
 ## Installation
 
+The package is available from CRAN and can be installed with:
+
+``` r
+install.packages("sdtm.oak")
+```
+
 You can install the development version of `{sdtm.oak}` from
 [GitHub](https://github.com/pharmaverse/sdtm.oak/) with:
 
 ``` r
 # install.packages("remotes")
 remotes::install_github("pharmaverse/sdtm.oak")
 ```
+
+## Challenges with SDTM at the Industry Level
+
+- Raw Data Structure: Data from different EDC systems come in varying
+  structures, with different variable names, dataset names, etc.
+
+- Varying Data Collection Standards: Despite the availability of CDASH,
+  pharmaceutical companies still create different eCRFs using CDASH
+  standards.
+
+Due to the differences in raw data structures and data collection
+standards, it may seem impossible to develop a common approach for
+programming SDTM datasets.
+
+## GOAL
+
+`{sdtm.oak}` aims to address this issue by providing an EDC-agnostic,
+standards-agnostic solution. It is an open-source R package that offers
+a framework for the modular programming of SDTM in R. With future
+releases; it will also strive to automate the creation of SDTM datasets
+based on the metadata-driven approach using standard SDTM
+specifications.
+
+## Scope
+
+Our goal is to use `{sdtm.oak}` to program most of the domains specified
+in SDTMIG (Study Data Tabulation Model Implementation Guide: Human
+Clinical Trials) and SDTMIG-AP (Study Data Tabulation Model
+Implementation Guide: Associated Persons). This R package is based on
+the core concept of `algorithms`, implemented as functions capable of
+carrying out the SDTM mappings for any domains listed in the CDISC
+SDTMIG and across different versions of SDTM IGs. The design of these
+functions allows users to specify a raw dataset and a variable name(s)
+as parameters, making it EDC (Electronic Data Capture) agnostic. As long
+as the raw dataset and variable name(s) exist, `{sdtm.oak}` will execute
+the SDTM mapping using the selected function. It’s important to note
+that `{sdtm.oak}` may not handle sponsor-specific details related to
+managing metadata for LAB tests, unit conversions, and coding
+information, as many companies have unique business processes. With
+subsequent releases, strive to automate SDTM creation using a
+metadata-driven approach based on a standard SDTM specification format.
+
+## Road Map
+
+This Release: The V0.1 release of `{sdtm.oak}` users can create the
+majority of the SDTM domains. Domains that are NOT in scope for the V0.1
+release are DM, Trial Design Domains, SV, SE, RELREC, Associated Person
+domains, and EPOCH Variable across all domains.
+
+Subsequent Releases: We are planning to develop the below features in
+the subsequent releases.  
+- Functions required to derive reference date variables in the DM
+domain.  
+- Metadata driven automation based on the standardized SDTM
+specification.  
+- Functions required to program the EPOCH Variable.  
+- Functions to derive standard units and results based on metadata.
+
+## References and Documentation
+
+- Please go to
+  [Algorithms](https://pharmaverse.github.io/sdtm.oak/articles/algorithms.html)
+  article to learn about Algorithms.
+- Please go to [Create Events
+  Domain](https://pharmaverse.github.io/sdtm.oak/articles/events_domain.html)
+  to learn about step by step process to create an Events domain.
+- Please go to [Create Findings
+  Domain](https://pharmaverse.github.io/sdtm.oak/articles/findings_domain.html)
+  to learn about step by step process to create an Events domain.
+
+## Feedback
+
+We ask users to follow the mentioned approach and try `{sdtm.oak}` to
+map any SDTM domains supported in this release. Users can also utilize
+the test data in the package to become familiar with the concepts before
+attempting on their own data. Please get in touch with us using one of
+the recommended approaches listed below:
+
+- [Slack](https://oakgarden.slack.com/)
+- [GitHub](https://github.com/pharmaverse/sdtm.oak/issues)
+
+## Acknowledgments
+
+We thank the contributors and authors of the package. We also thank the
+CDISC COSA for sponsoring the `{sdtm.oak}`. Additionally, we would like
+to sincerely thank the volunteers from Roche, Pfizer, GSK, Vertex, and
+Merck for their valuable input as integral members of the CDISC COSA -
+OAK leadership team.

_pkgdown.yml

@@ -57,3 +57,9 @@ reference:
 - title: Package global state
   contents:
   - clear_cache
+
+authors:
+  Ramiro Magno:
+    href: https://www.pattern.institute/team/rmagno/
+  Pattern Institute:
+    href: https://www.pattern.institute

inst/WORDLIST

@@ -65,3 +65,10 @@ RFXSTDTC
 TPT
 xxTPT
 APSC
+CDASH
+COSA
+IGs
+RELREC
+SDTMIG
+SV
+GSK