tests/testthat/helper-old-version-gs_power_ahr_.R

In 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.
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#' @importFrom tibble tibble
#' @importFrom gsDesign gsDesign sfLDOF
#' @importFrom stats qnorm
#' @importFrom dplyr select arrange desc right_join
NULL
#' Group sequential design power using average hazard ratio under non-proportional hazards
#'
#' @param enrollRates enrollment rates
#' @param failRates failure and dropout rates
#' @param ratio Experimental:Control randomization ratio (not yet implemented)
#' @param events Targeted events at each analysis
#' @param analysisTimes Minimum time of analysis
#' @param binding indicator of whether futility bound is binding; default of FALSE is recommended
#' @param upper Function to compute upper bound
#' @param upar Parameter passed to \code{upper()}
#' @param lower Function to compute lower bound
#' @param lpar Parameter passed to \code{lower()}
#' @param test_upper indicator of which analyses should include an upper (efficacy) bound; single value of TRUE (default) indicates all analyses;
#' otherwise, a logical vector of the same length as \code{info} should indicate which analyses will have an efficacy bound
#' @param test_lower indicator of which analyses should include an lower bound; single value of TRUE (default) indicates all analyses;
#' single value FALSE indicated no lower bound; otherwise, a logical vector of the same length as \code{info} should indicate which analyses will have a
#' lower bound
#' @param r  Integer, at least 2; default of 18 recommended by Jennison and Turnbull
#' @param tol Tolerance parameter for boundary convergence (on Z-scale)
#' @section Specification:
#' \if{latex}{
#'  \itemize{
#'    \item Calculate information and effect size based on AHR approximation using \code{gs_info_ahr()}.
#'    \item Return a tibble of with columns Analysis, Bound, Z, Probability, theta,
#'     Time, AHR, Events and  contains a row for each analysis and each bound.
#'   }
#' }
#' \if{html}{The contents of this section are shown in PDF user manual only.}
#'
#' @return a \code{tibble} with columns \code{Analysis, Bound, Z, Probability, theta, Time, AHR, Events}.
#' Contains a row for each analysis and each bound.
#' @details
#' Bound satisfy input upper bound specification in \code{upper, upar} and lower bound specification in \code{lower, lpar}.
#' The \code{AHR()} function computes statistical information at targeted event times.
#' The \code{tEvents()} function is used to get events and average HR at targeted \code{analysisTimes}.
#'
#' @noRd
#'
#' @examples
#' library(gsDesign2)
#' library(dplyr)
#'
#' gs_power_ahr() |> dplyr::filter(abs(Z) < Inf)
#'
#' # 2-sided symmetric O'Brien-Fleming spending bound
#' # NOT CURRENTLY WORKING
#' gs_power_ahr(
#'   analysisTimes = c(12, 24, 36),
#'   binding = TRUE,
#'   upper = gs_spending_bound,
#'   upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
#'   lower = gs_spending_bound,
#'   lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL)
#' )
#'
gs_power_ahr_ <- function(enrollRates = tibble::tibble(
                            Stratum = "All",
                            duration = c(2, 2, 10),
                            rate = c(3, 6, 9)
                          ),
                          failRates = tibble::tibble(
                            Stratum = "All",
                            duration = c(3, 100),
                            failRate = log(2) / c(9, 18),
                            hr = c(.9, .6),
                            dropoutRate = rep(.001, 2)
                          ),
                          ratio = 1, # Experimental:Control randomization ratio
                          events = c(30, 40, 50), # Targeted events of analysis
                          analysisTimes = NULL, # Targeted times of analysis
                          binding = FALSE,
                          upper = gs_spending_bound,
                          # Default is Lan-DeMets approximation of
                          upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
                          lower = gs_spending_bound,
                          lpar = list(sf = gsDesign::sfLDOF, total_spend = NULL), # Futility only at IA1
                          test_upper = TRUE,
                          test_lower = TRUE,
                          r = 18,
                          tol = 1e-6) {
  x <- gs_info_ahr_(
    enrollRates = enrollRates,
    failRates = failRates,
    ratio = ratio,
    events = events,
    analysisTimes = analysisTimes
  )
  return(gs_power_npe_(
    theta = x$theta, info = x$info, info0 = x$info0, binding = binding,
    upper = upper, lower = lower, upar = upar, lpar = lpar,
    test_upper = test_upper, test_lower = test_lower,
    r = r, tol = tol
  ) |>
    dplyr::right_join(x |> dplyr::select(-c(info, info0, theta)), by = "Analysis") |>
    dplyr::select(c(Analysis, Bound, Time, Events, Z, Probability, AHR, theta, info, info0)) |>
    dplyr::arrange(dplyr::desc(Bound), Analysis))
}