Merge branch 'main' into 1324-feature-request-varying-decimal-precisi…

…on-in-a_summary

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: tern
 Title: Create Common TLGs Used in Clinical Trials
-Version: 0.9.6.9016
-Date: 2024-11-20
+Version: 0.9.6.9017
+Date: 2024-12-03
 Authors@R: c(
     person("Joe", "Zhu", , "[email protected]", role = c("aut", "cre")),
     person("Daniel", "Sabanés Bové", , "[email protected]", role = "aut"),

NEWS.md

@@ -1,4 +1,4 @@
-# tern 0.9.6.9016
+# tern 0.9.6.9017
 
 ### Enhancements
 * Added the `denom` parameter to `s_count_cumulative()`, `s_count_missed_doses()`, and `s_count_occurrences_by_grade()`.

R/summarize_glm_count.R

@@ -84,6 +84,20 @@ NULL
 #'       rate = "Rate", rate_ci = "Rate CI", rate_ratio = "Rate Ratio",
 #'       rate_ratio_ci = "Rate Ratio CI", pval = "p value"
 #'     )
+#'   ) %>%
+#'   summarize_glm_count(
+#'     vars = "AVAL",
+#'     variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = c("REGION1")),
+#'     conf_level = 0.95,
+#'     distribution = "negbin",
+#'     rate_mean_method = "emmeans",
+#'     var_labels = "Adjusted (NB) exacerbation rate (per year)",
+#'     table_names = "adjNB",
+#'     .stats = c("rate", "rate_ci", "rate_ratio", "rate_ratio_ci", "pval"),
+#'     .labels = c(
+#'       rate = "Rate", rate_ci = "Rate CI", rate_ratio = "Rate Ratio",
+#'       rate_ratio_ci = "Rate Ratio CI", pval = "p value"
+#'     )
 #'   )
 #'
 #' build_table(lyt = lyt, df = anl)
@@ -322,7 +336,6 @@ h_glm_poisson <- function(.var,
                           weights) {
   arm <- variables$arm
   covariates <- variables$covariates
-  offset <- .df_row[[variables$offset]]
 
   formula <- stats::as.formula(paste0(
     .var, " ~ ",
@@ -332,12 +345,21 @@ h_glm_poisson <- function(.var,
     arm
   ))
 
-  glm_fit <- stats::glm(
-    formula = formula,
-    offset = offset,
-    data = .df_row,
-    family = stats::poisson(link = "log")
-  )
+  if (is.null(variables$offset)) {
+    glm_fit <- stats::glm(
+      formula = formula,
+      data = .df_row,
+      family = stats::poisson(link = "log")
+    )
+  } else {
+    offset <- .df_row[[variables$offset]]
+    glm_fit <- stats::glm(
+      formula = formula,
+      offset = offset,
+      data = .df_row,
+      family = stats::poisson(link = "log")
+    )
+  }
 
   emmeans_fit <- emmeans::emmeans(
     glm_fit,
@@ -366,7 +388,6 @@ h_glm_quasipoisson <- function(.var,
                                weights) {
   arm <- variables$arm
   covariates <- variables$covariates
-  offset <- .df_row[[variables$offset]]
 
   formula <- stats::as.formula(paste0(
     .var, " ~ ",
@@ -376,13 +397,21 @@ h_glm_quasipoisson <- function(.var,
     arm
   ))
 
-  glm_fit <- stats::glm(
-    formula = formula,
-    offset = offset,
-    data = .df_row,
-    family = stats::quasipoisson(link = "log")
-  )
-
+  if (is.null(variables$offset)) {
+    glm_fit <- stats::glm(
+      formula = formula,
+      data = .df_row,
+      family = stats::quasipoisson(link = "log")
+    )
+  } else {
+    offset <- .df_row[[variables$offset]]
+    glm_fit <- stats::glm(
+      formula = formula,
+      offset = offset,
+      data = .df_row,
+      family = stats::quasipoisson(link = "log")
+    )
+  }
   emmeans_fit <- emmeans::emmeans(
     glm_fit,
     specs = arm,
@@ -410,7 +439,6 @@ h_glm_negbin <- function(.var,
                          weights) {
   arm <- variables$arm
   covariates <- variables$covariates
-
   formula <- stats::as.formula(paste0(
     .var, " ~ ",
     " + ",
@@ -419,6 +447,26 @@ h_glm_negbin <- function(.var,
     arm
   ))
 
+  if (is.null(variables$offset)) {
+    formula <- stats::as.formula(paste0(
+      .var, " ~ ",
+      " + ",
+      paste(covariates, collapse = " + "),
+      " + ",
+      arm
+    ))
+  } else {
+    offset <- variables$offset
+    formula_txt <- sprintf(
+      "%s ~ %s + %s + offset(%s)",
+      .var,
+      arm, paste0(covariates, collapse = " + "), offset
+    )
+    formula <- stats::as.formula(
+      formula_txt
+    )
+  }
+
   glm_fit <- MASS::glm.nb(
     formula = formula,
     data = .df_row,

tests/testthat/_snaps/summarize_glm_count.md

@@ -8,15 +8,15 @@
       2  0.10946932 0.09613709  1.1386793  2.548369e-01     ARMB: Placebo
       3 -0.04371457 0.10265030 -0.4258591  6.702105e-01 ARMC: Combination
 
-# h_glm_poisson emmeans-fit works with healthy input
+# h_glm_poisson emmeans-fit works with healthy input no offset
 
     Code
       res
     Output
-        ARMCD     rate std.error  df null statistic       p.value
-      1 ARM A 8.206105 0.5773795 Inf    1  29.91594 1.220818e-196
-      2 ARM B 9.155436 0.5997925 Inf    1  33.80055 1.935734e-250
-      3 ARM C 7.855107 0.5871181 Inf    1  27.57650 2.129731e-167
+        ARMCD     rate std.error  df null statistic      p.value
+      1 ARM A 2.927536 0.2059807 Inf    1  15.26670 1.274641e-52
+      2 ARM B 3.191781 0.2091005 Inf    1  17.71547 3.185540e-70
+      3 ARM C 3.086207 0.2306739 Inf    1  15.07747 2.278432e-51
 
 # h_glm_poisson glm-fit works with healthy input with covariates
 
@@ -68,22 +68,22 @@
       2     B: Placebo 14.11838  5.392442 Inf    1  6.931571 4.161914e-12
       3 C: Combination 10.59153  4.074355 Inf    1  6.135104 8.510352e-10
 
-# h_glm_negbin glm-fit works with healthy input
+# h_glm_negbin glm-fit works with healthy input with offset
 
     Code
       res
     Output
-           Estimate        SE    z_value           Pr                coefs
-      1 1.005041594 0.1992268 5.04471149 4.542062e-07          (Intercept)
-      2 0.007741431 0.1919877 0.04032253 9.678360e-01          REGION1Asia
-      3 0.317703043 0.2360653 1.34582686 1.783584e-01       REGION1Eurasia
-      4 0.591541717 0.4058327 1.45759983 1.449509e-01        REGION1Europe
-      5 0.117240049 0.2196300 0.53380718 5.934749e-01 REGION1North America
-      6 0.139971334 0.2348685 0.59595610 5.512046e-01 REGION1South America
-      7 0.113082781 0.1056295 1.07056107 2.843668e-01        ARMB: Placebo
-      8 0.026817451 0.1131811 0.23694292 8.127011e-01    ARMC: Combination
-
-# h_glm_negbin emmeans-fit works with healthy input
+          Estimate        SE   z_value           Pr                coefs
+      1 2.56211826 0.3969418 6.4546439 1.084737e-10          (Intercept)
+      2 0.12359121 0.2085554 0.5926060 5.534448e-01        ARMB: Placebo
+      3 0.05569230 0.2247874 0.2477554 8.043237e-01    ARMC: Combination
+      4 0.05684153 0.3845557 0.1478109 8.824920e-01          REGION1Asia
+      5 1.84806287 0.4849801 3.8105953 1.386326e-04       REGION1Eurasia
+      6 2.04520468 0.9029032 2.2651428 2.350392e-02        REGION1Europe
+      7 0.44720215 0.4417735 1.0122884 3.114002e-01 REGION1North America
+      8 1.21153294 0.4690914 2.5827223 9.802419e-03 REGION1South America
+
+# h_glm_negbin emmeans-fit works with healthy input no offset
 
     Code
       res
@@ -189,12 +189,12 @@
       [1] 73
       
       $rate
-      [1] 3.720373
+      [1] 37.35687
       attr(,"label")
       [1] "Adjusted Rate"
       
       $rate_ci
-      [1] 3.048181 4.540799
+      [1] 24.59065 56.75067
       attr(,"label")
       [1] "95% CI"
       
@@ -257,27 +257,27 @@
       [1] 73
       
       $rate
-      [1] 3.720373
+      [1] 37.35687
       attr(,"label")
       [1] "Adjusted Rate"
       
       $rate_ci
-      [1] 3.048181 4.540799
+      [1] 24.59065 56.75067
       attr(,"label")
       [1] "95% CI"
       
       $rate_ratio
-      [1] 0.8930767 0.9173508
+      [1] 0.8837410 0.9343549
       attr(,"label")
       [1] "Adjusted Rate Ratio"
       
       $rate_ratio_ci
-      [1] 0.7260672 0.7381034 1.0985017 1.1401282
+      [1] 0.5872220 0.6062612 1.3299880 1.4400049
       attr(,"label")
       [1] "95% CI"
       
       $pval
-      [1] 0.2843668 0.4367453
+      [1] 0.5534448 0.7583367
       attr(,"label")
       [1] "p-value"
       
@@ -322,5 +322,5 @@
         7                                       8 (10.96%)     2 (2.90%)      0 (0.00%)   
         10                                       0 (0.00%)     0 (0.00%)      1 (1.72%)   
       Unadjusted exacerbation rate (per year)                                             
-        Rate                                      3.1918        2.9275          3.0862    
+        Rate                                      18.6539       21.8760        22.6006    
 

tests/testthat/test-summarize_glm_count.R

@@ -18,15 +18,15 @@ testthat::test_that("h_glm_poisson glm-fit works with healthy input", {
   testthat::expect_snapshot(res)
 })
 
-testthat::test_that("h_glm_poisson emmeans-fit works with healthy input", {
+testthat::test_that("h_glm_poisson emmeans-fit works with healthy input no offset", {
   anl <- tern_ex_adtte %>%
     filter(PARAMCD == "TNE")
   anl$AVAL_f <- as.factor(anl$AVAL)
 
   result <- h_glm_count(
     .var = "AVAL",
     .df_row = anl,
-    variables = list(arm = "ARMCD", offset = "lgTMATRSK", covariates = NULL),
+    variables = list(arm = "ARMCD", covariates = NULL),
     distribution = "poisson"
   )
   mat1 <- as.data.frame(broom::tidy(result$emmeans_fit))
@@ -144,7 +144,7 @@ testthat::test_that("h_glm_quasipoisson fails wrong inputs", {
   )
 })
 
-testthat::test_that("h_glm_negbin glm-fit works with healthy input", {
+testthat::test_that("h_glm_negbin glm-fit works with healthy input with offset", {
   anl <- tern_ex_adtte %>%
     filter(PARAMCD == "TNE")
   anl$AVAL_f <- as.factor(anl$AVAL)
@@ -164,15 +164,15 @@ testthat::test_that("h_glm_negbin glm-fit works with healthy input", {
   testthat::expect_snapshot(res)
 })
 
-testthat::test_that("h_glm_negbin emmeans-fit works with healthy input", {
+testthat::test_that("h_glm_negbin emmeans-fit works with healthy input no offset", {
   anl <- tern_ex_adtte %>%
     filter(PARAMCD == "TNE")
   anl$AVAL_f <- as.factor(anl$AVAL)
 
   result <- h_glm_negbin(
     .var = "AVAL",
     .df_row = anl,
-    variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = c("REGION1"))
+    variables = list(arm = "ARM", covariates = c("REGION1"))
   )
   mat1 <- as.data.frame(broom::tidy(result$emmeans_fit))