Prepare Release

.lintr

@@ -1,6 +1,5 @@
 linters: linters_with_defaults(
   line_length_linter = line_length_linter(120),
-  cyclocomp_linter = NULL,
   object_usage_linter = NULL,
   commented_code_linter = NULL,
   object_name_linter = NULL

DESCRIPTION

@@ -1,7 +1,6 @@
 Title: Matching Adjusted Indirect Comparison
 Package: maicplus
 Version: 0.1.2
-Date: 2024-10-29
 Authors@R: c(
    person(
       given = "Gregory",
@@ -75,5 +74,5 @@ Encoding: UTF-8
 Language: en-US
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.2.9000
+RoxygenNote: 7.3.2
 Config/testthat/edition: 3

NEWS.md

@@ -1,5 +1,12 @@
 # maicplus 0.1.2
 
+- Changed the link function to binomial for RD and RR in binary analysis
+- Updated vignette to recommend against bootstrap in unanchored analysis with citation
+- Fix events percentage to properly incorporate weights in the report table for binary analysis
+- Replace quantile function to use matrixStats package instead of desctools package
+- Added testing PH assumptions vignette
+- Fixed the issues with merging weights using match function
+
 # maicplus 0.1.1
 
 - bug fixes

R/maic_anchored.R

@@ -485,9 +485,9 @@ maic_anchored_binary <- function(res,
   # ~~~ Analysis table
   # : set up proper link
   glm_link <- switch(eff_measure,
-    "RD" = binomial(link = "identity"),
-    "RR" = binomial(link = "log"),
-    "OR" = binomial(link = "logit")
+    "RD" = "identity",
+    "RR" = "log",
+    "OR" = "logit"
   )
   res_template <- list(
     est = NA,
@@ -498,9 +498,9 @@ maic_anchored_binary <- function(res,
   )
 
   # : fit glm for binary outcome and robust estimate with weights
-  binobj_ipd <- glm(RESPONSE ~ ARM, ipd, family = glm_link)
-  binobj_ipd_adj <- suppressWarnings(glm(RESPONSE ~ ARM, ipd, weights = weights, family = glm_link))
-  binobj_agd <- glm(RESPONSE ~ ARM, pseudo_ipd, family = glm_link)
+  binobj_ipd <- glm(RESPONSE ~ ARM, ipd, family = binomial(link = glm_link))
+  binobj_ipd_adj <- suppressWarnings(glm(RESPONSE ~ ARM, ipd, weights = weights, family = binomial(link = glm_link)))
+  binobj_agd <- glm(RESPONSE ~ ARM, pseudo_ipd, family = binomial(link = glm_link))
 
   bin_robust_cov <- sandwich::vcovHC(binobj_ipd_adj, type = binary_robust_cov_type)
   bin_robust_coef <- lmtest::coeftest(binobj_ipd_adj, vcov. = bin_robust_cov)
@@ -571,7 +571,7 @@ maic_anchored_binary <- function(res,
       }
 
       boot_binobj_dat_adj <- suppressWarnings(
-        glm(RESPONSE ~ ARM, boot_ipd, weights = boot_ipd$weights, family = glm_link)
+        glm(RESPONSE ~ ARM, boot_ipd, weights = boot_ipd$weights, family = binomial(link = glm_link))
       )
       boot_AC_est <- coef(boot_binobj_dat_adj)[2]
       boot_AC_var <- vcov(boot_binobj_dat_adj)[2, 2]

R/maic_unanchored.R

@@ -355,9 +355,9 @@ maic_unanchored_binary <- function(res,
   # ~~~ Analysis table
   # : set up proper link
   glm_link <- switch(eff_measure,
-    "RD" = binomial(link = "identity"),
-    "RR" = binomial(link = "log"),
-    "OR" = binomial(link = "logit")
+    "RD" = "identity",
+    "RR" = "log",
+    "OR" = "logit"
   )
   transform_estimate <- switch(eff_measure,
     "RD" = function(x) x * 100,
@@ -366,8 +366,8 @@ maic_unanchored_binary <- function(res,
   )
 
   # : fit glm for binary outcome and robust estimate with weights
-  binobj_dat <- glm(RESPONSE ~ ARM, dat, family = glm_link)
-  binobj_dat_adj <- suppressWarnings(glm(RESPONSE ~ ARM, dat, weights = weights, family = glm_link))
+  binobj_dat <- glm(RESPONSE ~ ARM, dat, family = binomial(link = glm_link))
+  binobj_dat_adj <- suppressWarnings(glm(RESPONSE ~ ARM, dat, weights = weights, family = binomial(link = glm_link)))
 
   bin_robust_cov <- sandwich::vcovHC(binobj_dat_adj, type = binary_robust_cov_type)
   bin_robust_coef <- lmtest::coeftest(binobj_dat_adj, vcov. = bin_robust_cov)
@@ -424,7 +424,9 @@ maic_unanchored_binary <- function(res,
       }
       boot_dat <- rbind(boot_ipd, pseudo_ipd)
       boot_dat$ARM <- factor(boot_dat$ARM, levels = c(trt_agd, trt_ipd))
-      boot_binobj_dat_adj <- suppressWarnings(glm(RESPONSE ~ ARM, boot_dat, weights = weights, family = glm_link))
+      boot_binobj_dat_adj <- suppressWarnings(
+        glm(RESPONSE ~ ARM, boot_dat, weights = weights, family = binomial(link = glm_link))
+      )
       c(est = coef(boot_binobj_dat_adj)[2], var = vcov(boot_binobj_dat_adj)[2, 2])
     }
 

R/matching.R

@@ -374,7 +374,7 @@ plot_weights_ggplot <- function(weighted_data, bin_col, vline_col,
     ggplot2::ylab("Frequency") +
     ggplot2::xlab("Weight")
 
-  return(hist_plot)
+  hist_plot
 }
 
 

inst/WORDLIST

@@ -1,106 +1,61 @@
 ADaM
 Ades
-adrs
-adsl
 ADTTE
-adtte
+AGD
 AgD
-agd
 agg
 al
-Antoniou
-Azocar
 Belleli
 binarize
-bootswatch
 Bucher
-bucher
-Chrysostomos
 Comparator
 comparator
 confounders
-csv
-diagplot
+desctools
 doi
 DSU
 dummize
 dummized
 ECOG
 ESS
 et
-ffffff
 frac
 funder
-GBB
 ggplot
 github
 glm
-Gravestock
 Guyot
 Hoffmann
-href
+HTA
 hta
-https
-initializer
 io
 IPD
 ipd
-JCC
 jval
-Kalyvas
 Kaplan
-kaplan
-kmplot
-lch
-LFF
 Liu
 MAIC
 maic
 maicplus
-medSurv
-meier
-MGG
-Miranta
+matrixStats
 MSD
-multivariable
-navbar
-optim
 Paramater
-ph
 pharma
 Phillippo
-phillippo
-png
 Pre
 pre
 preprocessed
-Remiro
-reproducibility
 rmean
-rr
 Schoenfeld
-schoenfeld
-sd
-Seo
 Signorovitch
-signorovitch
-src
-ss
-survfit
 Texp
-THER
 TSD
 TTE
 tte
-twt
 Unanchored
 unanchored
-Univariable
 unscaled
 unstacked
 USUBJID
-vcovHC
 Welton
-ww
 XXXX
-yy

tests/testthat/_snaps/maic_anchored.md

@@ -414,7 +414,8 @@
     Output
       $model_before_ipd
 
-      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = ipd)
+      Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
+          data = ipd)
 
       Coefficients:
       (Intercept)         ARMA  
@@ -426,8 +427,8 @@
 
       $model_after_ipd
 
-      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = ipd, 
-          weights = weights)
+      Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
+          data = ipd, weights = weights)
 
       Coefficients:
       (Intercept)         ARMA  
@@ -439,7 +440,8 @@
 
       $model_agd
 
-      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = pseudo_ipd)
+      Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
+          data = pseudo_ipd)
 
       Coefficients:
       (Intercept)         ARMB  
@@ -553,7 +555,8 @@
     Output
       $model_before_ipd
 
-      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = ipd)
+      Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
+          data = ipd)
 
       Coefficients:
       (Intercept)         ARMA  
@@ -565,8 +568,8 @@
 
       $model_after_ipd
 
-      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = ipd, 
-          weights = weights)
+      Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
+          data = ipd, weights = weights)
 
       Coefficients:
       (Intercept)         ARMA  
@@ -578,7 +581,8 @@
 
       $model_agd
 
-      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = pseudo_ipd)
+      Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
+          data = pseudo_ipd)
 
       Coefficients:
       (Intercept)         ARMB  
@@ -692,7 +696,8 @@
     Output
       $model_before_ipd
 
-      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = ipd)
+      Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
+          data = ipd)
 
       Coefficients:
       (Intercept)         ARMA  
@@ -704,8 +709,8 @@
 
       $model_after_ipd
 
-      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = ipd, 
-          weights = weights)
+      Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
+          data = ipd, weights = weights)
 
       Coefficients:
       (Intercept)         ARMA  
@@ -717,7 +722,8 @@
 
       $model_agd
 
-      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = pseudo_ipd)
+      Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
+          data = pseudo_ipd)
 
       Coefficients:
       (Intercept)         ARMB  
@@ -831,7 +837,8 @@
     Output
       $model_before_ipd
 
-      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = ipd)
+      Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
+          data = ipd)
 
       Coefficients:
       (Intercept)         ARMA  
@@ -843,8 +850,8 @@
 
       $model_after_ipd
 
-      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = ipd, 
-          weights = weights)
+      Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
+          data = ipd, weights = weights)
 
       Coefficients:
       (Intercept)         ARMA  
@@ -856,7 +863,8 @@
 
       $model_agd
 
-      Call:  glm(formula = RESPONSE ~ ARM, family = glm_link, data = pseudo_ipd)
+      Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
+          data = pseudo_ipd)
 
       Coefficients:
       (Intercept)         ARMB