gsdesign {clinfun}R Documentation

Group Sequential Designs

Description

Functions to calculate sample size for group sequential designs

Usage

gsdesign.binomial(ifrac, pC, pE, sig.level = 0.05, power = 0.8,
  delta.eb=0.5, delta.fb = 0, alternative = c("two.sided", "one.sided"),
  tol=0.0001) 
gsdesign.normal(ifrac, delta, sd = 1, sig.level = 0.05, power = 0.8,
  delta.eb = 0.5, delta.fb = 0, alternative = c("two.sided", "one.sided"),
  tol=0.0001)
gsdesign.survival(ifrac, haz.ratio, sig.level = 0.05, power = 0.8,
  delta.eb = 0.5, delta.fb = 0, alternative = c("two.sided", "one.sided"),
  tol=0.0001)

Arguments

ifrac information fraction or the ratio of current sample size or number of events to the total sample size or number of events. This should be an increasing vector of numbers from 0 to 1 with the last one being 1. If just 1 is given a fixed sample design is derived.
pC prob of success of the standard therapy (for binomial data)
pE prob of success of the experimental therapy (for binomial data)
delta true difference in means (for normal data)
sd standard deviation (for normal data)
haz.ratio hazard ratio (for survival comparison)
sig.level significance level (type I error probability)
power power of test (1 minus type II error probability)
delta.eb power for efficacy boundary in the Pocock (power=0) to O'Brien-Fleming (power=0.5) family (default is 0.5)
delta.fb power for futility boundary in the Pocock (power=0) to O'Brien-Fleming (power=0.5) family (default is 0.5)
alternative one- or two-sided test.
tol tolerance level for multivariate normal probability computation.

Value

a list with ifrac, sig.level, power, alternative, delta.eb and:

efbdry the critical value to use at the different looks.
sample.size the sample size per arm for binomial/normal data.
num.events the total number of failures which should be converted to number of subjects using censoring proportion.

[Package clinfun version 0.8.4 Index]