gs_info_wlr: Information and effect size for Weighted Log-rank test

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

Information and effect size for Weighted Log-rank test

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_wlr(
  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,
  weight = wlr_weight_fh,
  approx = "asymptotic"
)

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
weight	weight of weighted log rank test "1"= unweighted, "n"= Gehan-Breslow, "sqrtN"= Tarone-Ware, "FH_p[a]_q[b]"= Fleming-Harrington with p=a and q=b
approx	approximate estimation method for Z statistics "event driven" = only work under proportional hazard model with log rank test "asymptotic"

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, N, Events, AHR, delta, sigma2, 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.

keaven/gsDesign2 documentation built on Oct. 13, 2022, 8:42 p.m.