# Copyright (c) 2022 Merck Sharp & Dohme Corp., a subsidiary of # Merck & Co., Inc., Rahway, NJ, USA. # # This file is part of the gsDesign2 program. # # gsDesign2 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 . #' @importFrom stats uniroot NULL #' Predict time at which a targeted event count is achieved #' #' \code{tEvents()} is made to match input format with \code{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. #' @param enrollRates Piecewise constant enrollment rates by stratum and time period. #' @param failRates Piecewise constant control group failure rates, duration for each piecewise constant period, #' hazard ratio for experimental vs control, and dropout rates by stratum and time period. #' @param targetEvents 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 `targetEvents`. #' @section Specification: #' \if{latex}{ #' \itemize{ #' \item Use root-finding routine with `AHR()` to find time at which targeted events accrue. #' \item Return a tibble with a single row with the output from `AHR()` got the specified output. #' } #' } #' @return A `tibble` with `Time` (computed to match events in `targetEvents`), `AHR` (average hazard ratio), #' `Events` (`targetEvents` input), info (information under given scenarios), #' and info0 (information under related null hypothesis) for each value of `totalDuration` input; #' #' @examples #' library(dplyr) #' library(gsDesign2) #' #' # Example 1: default #' gsDesign2:::tEvents_() #' #' # Example 2: check that result matches a finding using AHR() #' # Start by deriving an expected event count #' enrollRates <- #' tibble::tibble( #' Stratum = "All", #' duration = c(2, 2, 10), #' rate = c(3, 6, 9) * 5 #' ) #' failRates <- tibble::tibble( #' Stratum = "All", duration = c(3, 100), failRate = log(2) / c(9, 18), #' hr = c(.9, .6), dropoutRate = rep(.001, 2) #' ) #' totalDuration <- 20 #' xx <- gsDesign2:::AHR_(enrollRates, failRates, totalDuration) #' xx #' # Next we check that the function confirms the timing of the final analysis. #' gsDesign2:::tEvents_(enrollRates, failRates, targetEvents = xx$Events, interval = c(.5, 1.5) * xx$Time) #' #' @noRd tEvents_ <- function(enrollRates = tibble::tibble( Stratum = "All", duration = c(2, 2, 10), rate = c(3, 6, 9) * 5 ), failRates = tibble::tibble( Stratum = "All", duration = c(3, 100), failRate = log(2) / c(9, 18), hr = c(.9, .6), dropoutRate = rep(.001, 2) ), targetEvents = 150, ratio = 1, interval = c(.01, 100)) { res <- try( uniroot( function(x) { AHR_(enrollRates, failRates, x, ratio)$Events - targetEvents }, interval ) ) if (inherits(res, "try-error")) { stop("tEvents solution not found") } AHR_(enrollRates, failRates, res$root, ratio) }