gs_info_ahr: Information and effect size based on AHR approximation
In keaven/gsdmvn: Group sequential design with non-constant effect

Description Usage Arguments Details Value Examples

Based on piecewise enrollment rate, failure rate, and dropout rates computes approximate information and effect size using an average hazard ratio model.

gs_info_ahr(
  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(0.9, 0.6), dropoutRate = rep(0.001, 2)),
  ratio = 1,
  events = NULL,
  analysisTimes = NULL
)

`enrollRates`	enrollment rates
`failRates`	failure and dropout rates
`ratio`	Experimental:Control randomization ratio
`events`	Targeted minimum events at each analysis
`analysisTimes`	Targeted minimum study duration at each analysis

The AHR() function computes statistical information at targeted event times. The tEvents() function is used to get events and average HR at targeted analysisTimes.

a tibble with columns Analysis, Time, AHR, Events, theta, info, info0. info, info0 contains statistical information under H1, H0, respectively. For analysis k, Time[k] is the maximum of analysisTimes[k] and the expected time required to accrue the targeted events[k]. AHR is expected average hazard ratio at each analysis.

library(gsDesign)
library(gsDesign2)
# Only put in targeted events
gs_info_ahr(events = c(30, 40, 50))
# Only put in targeted analysis times
gs_info_ahr(analysisTimes = c(18, 27, 36))
# Some analysis times after time at which targeted events accrue
# Check that both Time >= input analysisTime and Events >= input events
gs_info_ahr(events = c(30, 40, 50), analysisTimes = c(16, 19, 26))
gs_info_ahr(events = c(30, 40, 50), analysisTimes = c(14, 20, 24))