General.tau: Information fractions under a generalized gamma survival distribution or a log-logistic survival distribution.

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

the interim analysis time (vector).

the recuritment duration.

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 \(F_0(y;\xi,\zeta)=1/(1+(y/\xi)^{-\zeta})\) for the control arm, where \(\xi\) = mu and \(\zeta\) = sigma.

rho

the power in the weight of the Harrington-Fleming statistic. \(\rho=0\) for the logrank test; \(\rho=1\) for the Wilcoxon test.

eta, theta

parameters of the entry distribution with \(\eta \ge -\theta/R\) and \(\eta >0\) (\(\theta=0\) 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

Run this code

# NOT RUN {
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)
# }

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