R/eEvents_.R

In keaven/gsDesign2: Group Sequential Design with Non-Constant Effect

#  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.
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#  You should have received a copy of the GNU General Public License
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#' @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 distribtuion
#' 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)
}