style r

hta-pharma · Sep 3, 2024 · 82164fd · 82164fd
1 parent 7fa809c
commit 82164fd
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Showing 3 changed files with 17 additions and 17 deletions.
diff --git a/R/binary-helper.R b/R/binary-helper.R
@@ -95,7 +95,7 @@ get_pseudo_ipd_binary <- function(binary_agd, format = c("stacked", "unstacked")
 #' @return a dataframe
 #' @export
 
-glm_makeup <- function(binobj, legend = "before matching", weighted = FALSE){
+glm_makeup <- function(binobj, legend = "before matching", weighted = FALSE) {
   ARM <- levels(binobj$data$ARM)
   if (!weighted) {
     N <- tapply(binobj$data$USUBJID, binobj$data$ARM, length)
@@ -107,12 +107,12 @@ glm_makeup <- function(binobj, legend = "before matching", weighted = FALSE){
   N.EVNT.PERC <- N.EVNT * 100 / N
   N.EVNT <- N.EVNT
 
-  data.frame(treatment = ARM,
-             type = rep(legend),
-             n = N,
-             events = N.EVNT,
-             `events%` = N.EVNT.PERC,
-             check.names = FALSE)
+  data.frame(
+    treatment = ARM,
+    type = rep(legend),
+    n = N,
+    events = N.EVNT,
+    `events%` = N.EVNT.PERC,
+    check.names = FALSE
+  )
 }
-
-
diff --git a/R/maic_anchored.R b/R/maic_anchored.R
@@ -254,7 +254,7 @@ maic_anchored_tte <- function(res,
   medSurv_agd <- medSurv_makeup(kmobj_agd, legend = "AgD, external", time_scale = time_scale)
   medSurv_out <- rbind(medSurv_ipd, medSurv_ipd_adj, medSurv_agd)
   medSurv_out <- cbind(medSurv_out[, 1:6], `events%` = medSurv_out$events * 100 / medSurv_out$n.max, medSurv_out[7:ncol(medSurv_out)])
-  medSurv_out <- cbind(trt_ind = c("C","B","A")[match(medSurv_out$treatment,levels(dat$ARM))], medSurv_out)
+  medSurv_out <- cbind(trt_ind = c("C", "B", "A")[match(medSurv_out$treatment, levels(dat$ARM))], medSurv_out)
 
   res$descriptive[["summary"]] <- medSurv_out
 
@@ -436,7 +436,7 @@ maic_anchored_binary <- function(res,
   glmDesc_ipd_adj <- glm_makeup(binobj_ipd_adj, legend = "IPD, after matching", weighted = TRUE)
   glmDesc_agd <- glm_makeup(binobj_agd, legend = "AgD, external", weighted = FALSE)
   glmDesc <- rbind(glmDesc_ipd, glmDesc_ipd_adj, glmDesc_agd)
-  glmDesc <- cbind(trt_ind = c("C","B","A")[match(glmDesc$treatment,levels(dat$ARM))], glmDesc)
+  glmDesc <- cbind(trt_ind = c("C", "B", "A")[match(glmDesc$treatment, levels(dat$ARM))], glmDesc)
   rownames(glmDesc) <- NULL
   res$descriptive[["summary"]] <- glmDesc
 
@@ -471,14 +471,14 @@ maic_anchored_binary <- function(res,
   res_AB_unadj <- bucher(res_AC_unadj, res_BC, conf_lv = 0.95)
 
   # transform
-  transform_ratio <- function(resobj){
+  transform_ratio <- function(resobj) {
     resobj$est <- exp(resobj$est)
     resobj$se <- sqrt((exp(resobj$se^2) - 1) * exp(2 * resobj$est + resobj$se^2)) # log normal parameterization
     resobj$ci_l <- exp(resobj$ci_l)
     resobj$ci_u <- exp(resobj$ci_u)
     resobj
   }
-  transform_absolute <- function(resobj){
+  transform_absolute <- function(resobj) {
     resobj$est <- resobj$est * 100
     resobj$se <- resobj$se * 100
     resobj$ci_l <- resobj$ci_l * 100

diff --git a/R/maic_unanchored.R b/R/maic_unanchored.R
@@ -215,7 +215,7 @@ maic_unanchored_tte <- function(res,
   medSurv_dat <- medSurv_makeup(kmobj_dat, legend = "Before matching", time_scale = time_scale)
   medSurv_dat_adj <- medSurv_makeup(kmobj_dat_adj, legend = "After matching", time_scale = time_scale)
   medSurv_out <- rbind(medSurv_dat, medSurv_dat_adj)
-  medSurv_out <- cbind(trt_ind = c("B","A")[match(medSurv_out$treatment,levels(dat$ARM))], medSurv_out)
+  medSurv_out <- cbind(trt_ind = c("B", "A")[match(medSurv_out$treatment, levels(dat$ARM))], medSurv_out)
 
   res$descriptive[["summary"]] <- medSurv_out
 
@@ -375,7 +375,7 @@ maic_unanchored_binary <- function(res,
   glmDesc_dat <- glm_makeup(binobj_dat, legend = "Before matching", weighted = FALSE)
   glmDesc_dat_adj <- glm_makeup(binobj_dat_adj, legend = "After matching", weighted = TRUE)
   glmDesc <- rbind(glmDesc_dat, glmDesc_dat_adj)
-  glmDesc <- cbind(trt_ind = c("B","A")[match(glmDesc$treatment,levels(dat$ARM))], glmDesc)
+  glmDesc <- cbind(trt_ind = c("B", "A")[match(glmDesc$treatment, levels(dat$ARM))], glmDesc)
   rownames(glmDesc) <- NULL
   res$descriptive[["summary"]] <- glmDesc
 
@@ -394,14 +394,14 @@ maic_unanchored_binary <- function(res,
   res_AB_unadj$pval <- summary(binobj_dat)$coefficients[2, "Pr(>|z|)"]
 
   # : transform
-  transform_ratio <- function(resobj){
+  transform_ratio <- function(resobj) {
     resobj$est <- exp(resobj$est)
     resobj$se <- sqrt((exp(resobj$se^2) - 1) * exp(2 * resobj$est + resobj$se^2)) # log normal parameterization
     resobj$ci_l <- exp(resobj$ci_l)
     resobj$ci_u <- exp(resobj$ci_u)
     resobj
   }
-  transform_absolute <- function(resobj){
+  transform_absolute <- function(resobj) {
     resobj$est <- resobj$est * 100
     resobj$se <- resobj$se * 100
     resobj$ci_l <- resobj$ci_l * 100