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#  This file is part of the gsDesign2 program.
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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 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
#' # Example 1: default
#' 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 <- AHR_(enrollRates, failRates, totalDuration)
#' xx
#' # Next we check that the function confirms the timing of the final analysis.
#' tEvents_(enrollRates, failRates, targetEvents = xx$Events, interval = c(.5, 1.5) * xx$Time)
#' @export
#'
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)
}