gs_info_ahr: Information and effect size based on AHR approximation

In Merck/gsdmvn: Group sequential design with non-constant effect

Information and effect size based on AHR approximation

Description

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

Usage

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
)

Arguments

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

Details

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

Value

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.

Specification

The contents of this section are shown in PDF user manual only.

Examples

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))

Merck/gsdmvn documentation built on June 30, 2023, 2:09 p.m.