Merge pull request #146 from Merck/pipe

Use base pipe operator

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: simtrial
 Type: Package
 Title: Clinical Trial Simulation
-Version: 0.3.0.6
+Version: 0.3.0.7
 Authors@R: c(
     person("Keaven", "Anderson", email = "[email protected]", role = c("aut")),
     person("Yilong", "Zhang", email = "[email protected]", role =  c("aut")),
@@ -20,7 +20,7 @@ Authors@R: c(
     person("John", "Blischak", role = c("ctb")),
     person("Merck & Co., Inc., Rahway, NJ, USA and its affiliates", role = "cph")
     )
-Description: simtrial provides some basic routines for simulating a
+Description: Provides some basic routines for simulating a
     clinical trial. The primary intent is to provide some tools to
     generate trial simulations for trials with time to event outcomes.
     Piecewise exponential failure rates and piecewise constant
@@ -34,14 +34,13 @@ BugReports: https://github.com/Merck/simtrial/issues
 Encoding: UTF-8
 LazyData: true
 VignetteBuilder: knitr
-Depends: R (>= 3.5.0)
+Depends: R (>= 4.1.0)
 Imports:
     Rcpp,
     data.table,
     doFuture,
     foreach,
     future,
-    magrittr,
     methods,
     mvtnorm,
     stats,

NAMESPACE

@@ -1,6 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-export("%>%")
 export(counting_process)
 export(cut_data_by_date)
 export(cut_data_by_event)
@@ -34,7 +33,6 @@ importFrom(data.table,setorderv)
 importFrom(data.table,uniqueN)
 importFrom(doFuture,"%dofuture%")
 importFrom(future,plan)
-importFrom(magrittr,"%>%")
 importFrom(methods,is)
 importFrom(mvtnorm,GenzBretz)
 importFrom(mvtnorm,pmvnorm)

R/MBdelayed.R

@@ -48,36 +48,36 @@
 #'
 #' # Set up time, event, number of event dataset for testing
 #' # with arbitrary weights
-#' ten <- MBdelayed %>% counting_process(arm = "experimental")
+#' ten <- MBdelayed |> counting_process(arm = "experimental")
 #' head(ten)
 #'
 #' # MaxCombo with logrank, FH(0,1), FH(1,1)
-#' ten %>%
-#'   fh_weight(rho_gamma = data.frame(rho = c(0, 0, 1), gamma = c(0, 1, 1)), return_corr = TRUE) %>%
+#' ten |>
+#'   fh_weight(rho_gamma = data.frame(rho = c(0, 0, 1), gamma = c(0, 1, 1)), return_corr = TRUE) |>
 #'   pvalue_maxcombo()
 #'
 #' # Magirr-Burman modestly down-weighted rank test with 6 month delay
 #' # First, add weights
-#' ten <- ten %>% mb_weight(6)
+#' ten <- ten |> mb_weight(6)
 #' head(ten)
 #'
 #' # Now compute test based on these weights
-#' ten %>%
+#' ten |>
 #'   summarize(
 #'     S = sum(o_minus_e * mb_weight),
 #'     V = sum(var_o_minus_e * mb_weight^2),
 #'     Z = S / sqrt(V)
-#'   ) %>%
+#'   ) |>
 #'   mutate(p = pnorm(Z))
 #'
 #' # Create 0 weights for first 6 months
-#' ten <- ten %>% mutate(w6 = 1 * (tte >= 6))
-#' ten %>%
+#' ten <- ten |> mutate(w6 = 1 * (tte >= 6))
+#' ten |>
 #'   summarize(
 #'     S = sum(o_minus_e * w6),
 #'     V = sum(var_o_minus_e * w6^2),
 #'     Z = S / sqrt(V)
-#'   ) %>%
+#'   ) |>
 #'   mutate(p = pnorm(Z))
 #'
 #' # Generate another dataset
@@ -100,5 +100,5 @@
 #'   )
 #' )
 #' # Cut data at 24 months after final enrollment
-#' MBdelayed2 <- ds %>% cut_data_by_date(max(ds$enroll_time) + 24)
+#' MBdelayed2 <- ds |> cut_data_by_date(max(ds$enroll_time) + 24)
 "MBdelayed"

R/counting_process.R

@@ -62,8 +62,6 @@
 #' @export
 #'
 #' @examples
-#' library(magrittr)
-#'
 #' # Example 1
 #' x <- data.frame(
 #'   stratum = c(rep(1, 10), rep(2, 6)),
@@ -75,7 +73,7 @@
 #'
 #' # Example 2
 #' x <- sim_pw_surv(n = 400)
-#' y <- cut_data_by_event(x, 150) %>% counting_process(arm = "experimental")
+#' y <- cut_data_by_event(x, 150) |> counting_process(arm = "experimental")
 #' # Weighted logrank test (Z-value and 1-sided p-value)
 #' z <- sum(y$o_minus_e) / sqrt(sum(y$var_o_minus_e))
 #' c(z, pnorm(z))

R/cut_data_by_date.R

@@ -31,7 +31,7 @@
 #' @examples
 #' # Use default enrollment and event rates and
 #' # cut at calendar time 5 after start of randomization
-#' sim_pw_surv(n = 20) %>% cut_data_by_date(5)
+#' sim_pw_surv(n = 20) |> cut_data_by_date(5)
 cut_data_by_date <- function(x, cut_date) {
   ans <- as.data.table(x)
   ans <- ans[enroll_time <= cut_date, ]

R/cut_data_by_event.R

@@ -31,10 +31,10 @@
 #'
 #' @examples
 #' # Use default enrollment and event rates at cut at 100 events
-#' x <- sim_pw_surv(n = 200) %>% cut_data_by_event(100)
+#' x <- sim_pw_surv(n = 200) |> cut_data_by_event(100)
 #' table(x$event, x$treatment)
 cut_data_by_event <- function(x, event) {
   cut_date <- get_cut_date_by_event(x, event)
-  ans <- x %>% cut_data_by_date(cut_date = cut_date)
+  ans <- x |> cut_data_by_date(cut_date = cut_date)
   return(ans)
 }

R/early_zero_weight.R

@@ -40,23 +40,23 @@
 #' library(gsDesign2)
 #'
 #' # Example 1: Unstratified
-#' sim_pw_surv(n = 200) %>%
-#'   cut_data_by_event(125) %>%
-#'   counting_process(arm = "experimental") %>%
-#'   early_zero_weight(early_period = 2) %>%
+#' sim_pw_surv(n = 200) |>
+#'   cut_data_by_event(125) |>
+#'   counting_process(arm = "experimental") |>
+#'   early_zero_weight(early_period = 2) |>
 #'   filter(row_number() %in% seq(5, 200, 40))
 #'
 #' # Example 2: Stratified
 #' n <- 500
 #' # Two strata
 #' stratum <- c("Biomarker-positive", "Biomarker-negative")
-#' prevelance_ratio <- c(0.6, 0.4)
+#' prevalence_ratio <- c(0.6, 0.4)
 #'
 #' # Enrollment rate
 #' enroll_rate <- define_enroll_rate(
 #'   stratum = rep(stratum, each = 2),
 #'   duration = c(2, 10, 2, 10),
-#'   rate = c(c(1, 4) * prevelance_ratio[1], c(1, 4) * prevelance_ratio[2])
+#'   rate = c(c(1, 4) * prevalence_ratio[1], c(1, 4) * prevalence_ratio[2])
 #' )
 #' enroll_rate$rate <- enroll_rate$rate * n / sum(enroll_rate$duration * enroll_rate$rate)
 #'
@@ -80,16 +80,16 @@
 #' sim_pw_surv(
 #'   n = n, # Sample size
 #'   # Stratified design with prevalence ratio of 6:4
-#'   stratum = tibble(stratum = stratum, p = prevelance_ratio),
+#'   stratum = tibble(stratum = stratum, p = prevalence_ratio),
 #'   # Randomization ratio
 #'   block = c("control", "control", "experimental", "experimental"),
 #'   enroll_rate = enroll_rate, # Enrollment rate
 #'   fail_rate = temp$fail_rate, # Failure rate
 #'   dropout_rate = temp$dropout_rate # Dropout rate
-#' ) %>%
-#'   cut_data_by_event(125) %>%
-#'   counting_process(arm = "experimental") %>%
-#'   early_zero_weight(early_period = 2, fail_rate = fail_rate) %>%
+#' ) |>
+#'   cut_data_by_event(125) |>
+#'   counting_process(arm = "experimental") |>
+#'   early_zero_weight(early_period = 2, fail_rate = fail_rate) |>
 #'   filter(row_number() %in% seq(5, 200, 40))
 early_zero_weight <- function(x, early_period = 4, fail_rate = NULL) {
   ans <- as.data.table(x)

R/fh_weight.R

@@ -88,8 +88,8 @@
 #'
 #' # Example 1
 #' # Use default enrollment and event rates at cut at 100 events
-#' x <- sim_pw_surv(n = 200) %>%
-#'   cut_data_by_event(100) %>%
+#' x <- sim_pw_surv(n = 200) |>
+#'   cut_data_by_event(100) |>
 #'   counting_process(arm = "experimental")
 #'
 #' # Compute logrank FH(0, 1)
@@ -104,9 +104,9 @@
 #' # Example 2
 #' # Use default enrollment and event rates at cut of 100 events
 #' set.seed(123)
-#' x <- sim_pw_surv(n = 200) %>%
-#'   cut_data_by_event(100) %>%
-#'   counting_process(arm = "experimental") %>%
+#' x <- sim_pw_surv(n = 200) |>
+#'   cut_data_by_event(100) |>
+#'   counting_process(arm = "experimental") |>
 #'   fh_weight(rho_gamma = data.frame(rho = c(0, 0), gamma = c(0, 1)), return_corr = TRUE)
 #'
 #' # Compute p-value for MaxCombo
@@ -118,11 +118,11 @@
 #' )[1]
 #'
 #' # Check that covariance is as expected
-#' x <- sim_pw_surv(n = 200) %>%
-#'   cut_data_by_event(100) %>%
+#' x <- sim_pw_surv(n = 200) |>
+#'   cut_data_by_event(100) |>
 #'   counting_process(arm = "experimental")
 #'
-#' x %>% fh_weight(
+#' x |> fh_weight(
 #'   rho_gamma = data.frame(
 #'     rho = c(0, 0),
 #'     gamma = c(0, 1)
@@ -131,7 +131,7 @@
 #' )
 #'
 #' # Off-diagonal element should be variance in following
-#' x %>% fh_weight(
+#' x |> fh_weight(
 #'   rho_gamma = data.frame(
 #'     rho = 0,
 #'     gamma = .5
@@ -140,10 +140,10 @@
 #' )
 #'
 #' # Compare off diagonal result with fh_weight()
-#' x %>% fh_weight(rho_gamma = data.frame(rho = 0, gamma = .5))
+#' x |> fh_weight(rho_gamma = data.frame(rho = 0, gamma = .5))
 fh_weight <- function(
-    x = sim_pw_surv(n = 200) %>%
-      cut_data_by_event(150) %>%
+    x = sim_pw_surv(n = 200) |>
+      cut_data_by_event(150) |>
       counting_process(arm = "experimental"),
     rho_gamma = data.frame(
       rho = c(0, 0, 1, 1),

R/get_cut_date_by_event.R

@@ -56,7 +56,7 @@
 #'   )
 #' )
 #'
-#' d <- get_cut_date_by_event(x %>% filter(stratum == "Positive"), event = 50)
+#' d <- get_cut_date_by_event(x |> filter(stratum == "Positive"), event = 50)
 #'
 #' y <- cut_data_by_date(x, cut_date = d)
 #' table(y$stratum, y$event)

R/mb_weight.R

@@ -75,14 +75,14 @@
 #'
 #' # Use default enrollment and event rates at cut at 100 events
 #' # For transparency, may be good to set either `delay` or `w_max` to `Inf`
-#' x <- sim_pw_surv(n = 200) %>%
-#'   cut_data_by_event(125) %>%
+#' x <- sim_pw_surv(n = 200) |>
+#'   cut_data_by_event(125) |>
 #'   counting_process(arm = "experimental")
 #'
 #' # Example 1
 #' # Compute Magirr-Burman weights with `delay = 6`
-#' ZMB <- x %>%
-#'   mb_weight(delay = 6, w_max = Inf) %>%
+#' ZMB <- x |>
+#'   mb_weight(delay = 6, w_max = Inf) |>
 #'   summarize(
 #'     S = sum(o_minus_e * mb_weight),
 #'     V = sum(var_o_minus_e * mb_weight^2),
@@ -94,8 +94,8 @@
 #'
 #' # Example 2
 #' # Now compute with maximum weight of 2 as recommended in Magirr, 2021
-#' ZMB2 <- x %>%
-#'   mb_weight(delay = Inf, w_max = 2) %>%
+#' ZMB2 <- x |>
+#'   mb_weight(delay = Inf, w_max = 2) |>
 #'   summarize(
 #'     S = sum(o_minus_e * mb_weight),
 #'     V = sum(var_o_minus_e * mb_weight^2),

R/pvalue_maxcombo.R

@@ -62,9 +62,9 @@
 #' head(xx)
 #'
 #' # MaxCombo power estimate for cutoff at max of targeted events, minimum follow-up
-#' p <- xx %>%
-#'   group_by(sim) %>%
-#'   group_map(~ pvalue_maxcombo(.x)) %>%
+#' p <- xx |>
+#'   group_by(sim) |>
+#'   group_map(~ pvalue_maxcombo(.x)) |>
 #'   unlist()
 #' mean(p < .025)
 pvalue_maxcombo <- function(

R/randomize_by_fixed_block.R

@@ -37,12 +37,12 @@
 #'
 #' # Example 1
 #' # 2:1 randomization with block size 3, treatments "A" and "B"
-#' data.frame(x = 1:10) %>% mutate(Treatment = randomize_by_fixed_block(block = c("A", "B", "B")))
+#' data.frame(x = 1:10) |> mutate(Treatment = randomize_by_fixed_block(block = c("A", "B", "B")))
 #'
 #' # Example 2
 #' # Stratified randomization
-#' data.frame(stratum = c(rep("A", 10), rep("B", 10))) %>%
-#'   group_by(stratum) %>%
+#' data.frame(stratum = c(rep("A", 10), rep("B", 10))) |>
+#'   group_by(stratum) |>
 #'   mutate(Treatment = randomize_by_fixed_block())
 randomize_by_fixed_block <- function(n = 10, block = c(0, 0, 1, 1)) {
   length_block <- length(block)

R/sim_fixed_n.R

@@ -94,24 +94,24 @@
 #'   rho_gamma = data.frame(rho = 0, gamma = c(0, 1))
 #' )
 #' # Get power approximation for FH, data cutoff combination
-#' xx %>%
-#'   group_by(cut, rho, gamma) %>%
+#' xx |>
+#'   group_by(cut, rho, gamma) |>
 #'   summarize(mean(z <= qnorm(.025)))
 #'
 #' # MaxCombo power estimate for cutoff at max of targeted events, minimum follow-up
-#' p <- xx %>%
-#'   filter(cut != "Targeted events") %>%
-#'   group_by(sim) %>%
-#'   group_map(~ pvalue_maxcombo(.x)) %>%
+#' p <- xx |>
+#'   filter(cut != "Targeted events") |>
+#'   group_by(sim) |>
+#'   group_map(~ pvalue_maxcombo(.x)) |>
 #'   unlist()
 #'
 #' mean(p < .025)
 #'
 #' # MaxCombo estimate for targeted events cutoff
-#' p <- xx %>%
-#'   filter(cut == "Targeted events") %>%
-#'   group_by(sim) %>%
-#'   group_map(~ pvalue_maxcombo(.x)) %>%
+#' p <- xx |>
+#'   filter(cut == "Targeted events") |>
+#'   group_by(sim) |>
+#'   group_map(~ pvalue_maxcombo(.x)) |>
 #'   unlist()
 #'
 #' mean(p < .025)

R/simfix2simpwsurv.R

@@ -41,8 +41,6 @@
 #' @export
 #'
 #' @examples
-#' library(magrittr)
-#'
 #' # Example 1
 #' # Convert standard input
 #' simfix2simpwsurv()
@@ -75,10 +73,10 @@
 #' )
 #'
 #' # Cut after 200 events and do a stratified logrank test
-#' dat <- sim %>%
-#'   cut_data_by_event(200) %>% # cut data
-#'   counting_process(arm = "experimental") %>% # convert format for tenFH
-#'   fh_weight(rho_gamma = data.frame(rho = 0, gamma = 0)) # stratified logrank
+#' dat <- sim |>
+#'   cut_data_by_event(200) |> # Cut data
+#'   counting_process(arm = "experimental") |> # Convert format for fh_weight()
+#'   fh_weight(rho_gamma = data.frame(rho = 0, gamma = 0)) # Stratified logrank
 simfix2simpwsurv <- function(
     # Failure rates as in sim_fixed_n()
     fail_rate = data.frame(