General.tau: Information fractions under a generalized gamma survival...

In SurvGSD: Group Sequential Design for a Clinical Trial with Censored Survival Data

Description

A function calculates information fractions with a generalized gamma survival distribution or a log-logistic survival distribution for a given dropout censoring probability.

Usage

General.tau(t, R, T, FUN.C, q, mu, sigma, rho, eta, theta)

Arguments

t	the interim analysis time (vector).
R	the recuritment duration.
T	the study duration.
FUN.C	the cumulative distribution function of dropout censoring. FUN.C = function(y) punif(y,0,h) for a uniform dropout censoring U(0,h); FUN.C = function(y) rep(0,length(y)) for assuming no dropout censoring.
q, mu, sigma	shape, location and scale parameters of an assumed generalized gamma distribution for the control arm. A character string q="LLG" indiactes an assumed log-logistic survival distribution F_0(y;ξ,ζ)=1/(1+(y/ξ)^{-ζ}) for the control arm, where ξ = mu and ζ = sigma.
rho	the power in the weight of the Harrington-Fleming statistic. ρ=0 for the logrank test; ρ=1 for the Wilcoxon test.
eta, theta	parameters of the entry distribution with η ≥ -θ/R and η >0 (θ=0 for the uniform dropout censoring).

Value

Info.fractions	information fractions at times of all the interim analyses.
Event.prob	the probability of events accumulated up to T.
Total.censor.prob	the probability of censoring including the dropout and administrative censoring.

Examples

General.tau(t=c(1,1.5,2,2.5), R=2, T=3, FUN.C=function(y) punif(y,0,7.018),
         q=1, mu=0.367, sigma=1, rho=0, eta=1, theta=0)
General.tau(t=c(1,1.5,2,2.5), R=2, T=3, FUN.C=function(y) punif(y,0,7.211),
         q="LLG", mu=1, sigma=1.75, rho=0, eta=1, theta=0)

SurvGSD documentation built on May 2, 2019, 11:12 a.m.