Here is one approach using simulation:
library(survival)
lrsim1 <- function(n, diff=0) {
n1 <- round( .8 * n )
n2 <- n - n1
tmp.df <- data.frame( group=rep(c('a','b'), c(n1,n2) ) )
tmp.time <- rexp( n, 1/rep(c(3,3+diff),c(n1,n2)) )
tmp.cens <- rexp( n, 1/4 )
tmp.df$time <- pmin(tmp.time,tmp.cens)
tmp.df$status <- ifelse( tmp.time <= tmp.cens, 1, 0 )
tmp.c <- survdiff( Surv(time,status) ~ group, data=tmp.df )$chisq
pchisq( tmp.c, 1, lower.tail=FALSE )
}
out1 <- replicate(1000, lrsim1(1000,1) )
mean( out1 <= 0.05 )
now just change what you want to change and rerun to estimate a new power (you
may want to only do 100 replicates until you find the general area of interest).
Hope this helps,
________________________________
From: [EMAIL PROTECTED] on behalf of Daniel Brewer
Sent: Thu 1/31/2008 8:53 AM
To: [EMAIL PROTECTED]
Subject: [R] Log rank test power calculations
Does anyone have any ideas how I could do a power calculation for a log
rank test. I would like to know what the suggested sample sizes would
be to pick a difference when the control to active are in a ratio of 80%
to 20%.
Thanks
Dan
--
**************************************************************
Daniel Brewer, Ph.D.
Institute of Cancer Research
Email: [EMAIL PROTECTED]
**************************************************************
The Institute of Cancer Research: Royal Cancer Hospital, a charitable Company
Limited by Guarantee, Registered in England under Company No. 534147 with its
Registered Office at 123 Old Brompton Road, London SW7 3RP.
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