R/expected_time.R

In gsDesign2: Group Sequential Design with Non-Constant Effect

#  Copyright (c) 2025 Merck & Co., Inc., Rahway, NJ, USA and its affiliates.
#  All rights reserved.
#
#  This file is part of the gsDesign2 program.
#
#  gsDesign2 is free software: you can re-distribute 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/>.

#' Predict time at which a targeted event count is achieved
#'
#' `expected_time()` is made to match input format with [ahr()] and to solve for the
#' time at which the expected accumulated events is equal to an input target.
#' Enrollment and failure rate distributions are specified as follows.
#' The piecewise exponential distribution allows a simple method to specify a distribution
#' and enrollment pattern
#' where the enrollment, failure and dropout rates changes over time.
#'
#' @inheritParams ahr
#' @param target_event The targeted number of events to be achieved.
#' @param ratio Experimental:Control randomization ratio.
#' @param interval An interval that is presumed to include the time at which
#'   expected event count is equal to `target_event`.
#'
#' @return A data frame with `Time` (computed to match events in `target_event`),
#'   `AHR` (average hazard ratio), `Events` (`target_event` input),
#'   `info` (information under given scenarios), and `info0`
#'   (information under related null hypothesis) for each value of
#'   `total_duration` input.
#'
#' @section Specification:
#' \if{latex}{
#'  \itemize{
#'    \item Use root-finding routine with `AHR()` to find time at which targeted events accrue.
#'    \item Return a data frame with a single row with the output from `AHR()` got the specified output.
#'    }
#'  }
#'
#' @export
#'
#' @examples
#' # Example 1 ----
#' # default
#' \donttest{
#' expected_time()
#' }
#'
#' # Example 2 ----
#' # check that result matches a finding using AHR()
#' # Start by deriving an expected event count
#' enroll_rate <- define_enroll_rate(duration = c(2, 2, 10), rate = c(3, 6, 9) * 5)
#' fail_rate <- define_fail_rate(
#'   duration = c(3, 100),
#'   fail_rate = log(2) / c(9, 18),
#'   hr = c(.9, .6),
#'   dropout_rate = .001
#' )
#' total_duration <- 20
#' xx <- ahr(enroll_rate, fail_rate, total_duration)
#' xx
#'
#' # Next we check that the function confirms the timing of the final analysis.
#' \donttest{
#' expected_time(enroll_rate, fail_rate,
#'   target_event = xx$event, interval = c(.5, 1.5) * xx$time
#' )
#' }
#'
#' # Example 3 ----
#' # In this example, we verify `expected_time()` by `ahr()`.
#' \donttest{
#' x <- ahr(
#'   enroll_rate = enroll_rate, fail_rate = fail_rate,
#'   ratio = 1, total_duration = 20
#' )
#'
#' cat("The number of events by 20 months is ", x$event, ".\n")
#'
#' y <- expected_time(
#'   enroll_rate = enroll_rate, fail_rate = fail_rate,
#'   ratio = 1, target_event = x$event
#' )
#'
#' cat("The time to get ", x$event, " is ", y$time, "months.\n")
#' }
expected_time <- function(
    enroll_rate = define_enroll_rate(
      duration = c(2, 2, 10),
      rate = c(3, 6, 9) * 5
    ),
    fail_rate = define_fail_rate(
      stratum = "All",
      duration = c(3, 100),
      fail_rate = log(2) / c(9, 18),
      hr = c(.9, .6),
      dropout_rate = rep(.001, 2)
    ),
    target_event = 150,
    ratio = 1,
    interval = c(.01, 100)) {
  ans <- NULL
  # calculate the difference between the targeted number of events and the
  # accumulated events at time `x`
  event_diff <- function(x) {
    ans <<- cache_fun(ahr, enroll_rate, fail_rate, x, ratio)
    ans$event - target_event
  }

  # Perform uniroot AHR() over total_duration ----
  tryCatch(uniroot(event_diff, interval, check.conv = TRUE), error = function(e) {
    stop("solution not found (", e$message, ")")
  })
  ans %>% select(-n)
}