```
# Copyright (c) 2022 Merck & Co., Inc., Rahway, NJ, USA and its affiliates. All rights reserved.
#
# This file is part of the simtrial program.
#
# simtrial is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#' @importFrom tibble tibble
NULL
#' The Piecewise Exponential Distribution
#'
#' The piecewise exponential distribution allows a simple method to specify a distribtuion
#' where the hazard rate changes over time. It is likely to be useful for conditions where
#' failure rates change, but also for simulations where there may be a delayed treatment
#' effect or a treatment effect that that is otherwise changing (e.g., decreasing) over time.
#' \code{rpwexp()} is to support simulation of both the Lachin and Foulkes (1986) sample size
#' method for (fixed trial duration) as well as the Kim and Tsiatis(1990) method
#' (fixed enrollment rates and either fixed enrollment duration or fixed minimum follow-up);
#' see \code{\link[gsDesign:nSurv]{gsDesign}}.
#'
#' Using the \code{cumulative=TRUE} option, enrollment times that piecewise constant over
#' time can be generated.
#'
#' @param n Number of observations to be generated.
#' @param fail_rate A tibble containing \code{duration} and \code{rate} variables.
#' \code{rate} specifies failure rates during the corresponding interval duration
#' specified in \code{duration}. The final interval is extended to be infinite
#' to ensure all observations are generated.
#'
#' @examples
#' library(tibble)
#'
#' # example 1
#' # exponential failure times
#' x <- rpwexp(n = 10000,
#' fail_rate = tibble(rate = 5, duration = 1))
#' plot(sort(x),(10000:1)/10001,
#' log = "y", main = "Exponential simulated survival curve",
#' xlab = "Time",ylab = "P{Survival}")
#'
#' # example 2
#'
#' # get 10k piecewise exponential failure times
#' # failure rates are 1 for time 0-.5, 3 for time .5 - 1 and 10 for >1.
#' # intervals specifies duration of each failure rate interval
#' # with the final interval running to infinity
#' x <- rpwexp(n = 1e4,
#' fail_rate = tibble(rate = c(1, 3, 10), duration = c(.5, .5, 1)))
#' plot(sort(x), (1e4:1)/10001,
#' log = "y", main = "PW Exponential simulated survival curve",
#' xlab = "Time", ylab = "P{Survival}")
#'
#' @export
rpwexp <- function(n = 100,
fail_rate = tibble(duration = c(1, 1), rate = c(10, 20))){
n_rate <- nrow(fail_rate)
if (n_rate == 1){
# set failure time to Inf if 0 failure rate
if(fail_rate$rate == 0){
ans <- rep(Inf, n)
}else{
# generate exponential failure time if non-0 failure rate
ans <- stats::rexp(n, fail_rate$rate)
}
}else{
# start of first failure rate interval
start_time <- 0
# ends of failure rate interval
end_time <- cumsum(fail_rate$duration)
# initiate vector for failure times
ans <- rep(0, n)
# index for event times not yet reached
indx <- rep(TRUE, n)
for(i in 1:n_rate){
# number of event times left to generate
n_event_left <- sum(indx)
# stop if event is arrived
if (n_event_left == 0){
break
}
# set failure time to Inf for interval i if 0 fail rate
if (fail_rate$rate[i] == 0){
ans[indx] = start_time + rep(Inf, n_event_left)
} else{
# generate exponential failure time for interval i if non-0 failure rate
ans[indx] <- start_time + stats::rexp(n_event_left, fail_rate$rate[i])
}
# skip this for last interval as all remaining times are generated there
if (i < n_rate){
start_time <- end_time[i]
# update index of event times not yet reached
indx <- (ans > end_time[i])
}
}
}
return(ans)
}
```

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