gs_power_nph: Group sequential design power under non-proportional hazards

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

Description

Group sequential design power under non-proportional hazards

Usage

gs_power_nph(
  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 = c(30, 40, 50),
  analysisTimes = NULL,
  maxEvents = 45,
  upper = gs_b,
  upar = gsDesign(k = length(events), test.type = 1, n.I = events, maxn.IPlan =
    maxEvents, sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lower = gs_b,
  lpar = c(qnorm(0.1), rep(-Inf, 2)),
  r = 18
)

Arguments

enrollRates	enrollment rates
failRates	failure and dropout rates
ratio	Experimental:Control randomization ratio (not yet implemented)
events	Targeted events at each analysis
analysisTimes	Not yet implemented
maxEvents	Final planned events
upper	Function to compute upper bound
upar	Parameter passed to upper()
lower	Function to compute lower bound
lpar	Parameter passed to lower()
r	Control for grid size; normally leave at default of r=18

Details

Need to be added

Value

a tibble with columns Analysis, Bound, Z, Probability, theta, Time, avehr, Events

Examples

library(gsDesign)
library(gsDesign2)
gs_power_nph() %>% filter(abs(Z) < Inf)

keaven/gsdmvn documentation built on May 30, 2021, 9:49 a.m.