diff --git a/R/corPFSOS.R b/R/corPFSOS.R
index 65cf6a9e..fbaaf060 100644
--- a/R/corPFSOS.R
+++ b/R/corPFSOS.R
@@ -277,8 +277,8 @@ corTrans <- function(transition) {
 #'   Initial parameters for optimization can be specified here.
 #'   See [exponential_transition()] or [weibull_transition()] for details.
 #' @param bootstrap (`flag`)\cr if `TRUE` computes confidence interval via bootstrap.
-#' @param bootstrap.n (`count`)\cr number of bootstrap samples.
-#' @param bootstrap.level (`proportion`)\cr confidence level for the confidence interval.
+#' @param bootstrap_n (`count`)\cr number of bootstrap samples.
+#' @param bootstrap_level (`proportion`)\cr confidence level for the confidence interval.
 #'
 #' @return The correlation of PFS and OS.
 #' @export
@@ -291,34 +291,35 @@ corTrans <- function(transition) {
 #'   accrual = list(param = "intensity", value = 7)
 #' )[[1]]
 #' corPFSOS(data, transition)
-corPFSOS <- function(data, transition, bootstrap = TRUE, bootstrap.n = 100, bootstrap.level = 0.95) {
+corPFSOS <- function(data, transition, bootstrap = TRUE, bootstrap_n = 100, bootstrap_level = 0.95) {
   assert_data_frame(data)
   assert_flag(bootstrap)
-  assert_count(bootstrap.n)
-  assert_number(bootstrap.level, lower = 0.01, upper = 0.999)
+  assert_count(bootstrap_n)
+  assert_number(bootstrap_level, lower = 0.01, upper = 0.999)
 
   trans <- estimateParams(data, transition)
-  res <- corTrans(transition)
+  res <- list("corPFSOS" = corTrans(transition))
   if (bootstrap) {
     future::plan(future::multisession, workers = max(1, parallel::detectCores() - 1))
-    ids <- lapply(1:bootstrap.n, function(x) sample(seq_len(nrow(data)), nrow(data), replace = TRUE))
+    ids <- lapply(1:bootstrap_n, function(x) sample(seq_len(nrow(data)), nrow(data), replace = TRUE))
     corBootstrap <- furrr::future_map_dbl(ids, ~ {
       furrr::furrr_options(
         globals = list(data = data, transition = transition),
         packages = c("simIDM")
       )
-      b_sample <- data[.x, ]
+      b_sample <- data[.x, ,drop = FALSE]
       prepared_data <- prepareData(b_sample)
       b_transition <- estimateParams(b_sample, transition)
       corTrans(b_transition)
     })
-    lowerQuantile <- (1 - bootstrap.level) / 2
-    upperQuantile <- lowerQuantile + bootstrap.level
+    lowerQuantile <- (1 - bootstrap_level) / 2
+    upperQuantile <- lowerQuantile + bootstrap_level
     c(stats::quantile(corBootstrap, lowerQuantile),
       "corPFSOS" = res,
       stats::quantile(corBootstrap, upperQuantile)
     )
-  } else {
-    c("corPFSOS" = res)
+    res$lower <- stats::quantile(corBootstrap, lowerQuantile)
+    res$upper <- stats::quantile(corBootstrap, upperQuantile)
   }
+  res
 }
diff --git a/man/corPFSOS.Rd b/man/corPFSOS.Rd
index 5d94df67..e3210005 100644
--- a/man/corPFSOS.Rd
+++ b/man/corPFSOS.Rd
@@ -8,8 +8,8 @@ corPFSOS(
   data,
   transition,
   bootstrap = TRUE,
-  bootstrap.n = 100,
-  bootstrap.level = 0.95
+  bootstrap_n = 100,
+  bootstrap_level = 0.95
 )
 }
 \arguments{
@@ -21,9 +21,9 @@ See \code{\link[=exponential_transition]{exponential_transition()}} or \code{\li
 
 \item{bootstrap}{(\code{flag})\cr if \code{TRUE} computes confidence interval via bootstrap.}
 
-\item{bootstrap.n}{(\code{count})\cr number of bootstrap samples.}
+\item{bootstrap_n}{(\code{count})\cr number of bootstrap samples.}
 
-\item{bootstrap.level}{(\code{proportion})\cr confidence level for the confidence interval.}
+\item{bootstrap_level}{(\code{proportion})\cr confidence level for the confidence interval.}
 }
 \value{
 The correlation of PFS and OS.