svylogrank: Compare survival distributions

Description

Computes a weighted version of the logrank and stratified logrank tests for comparing two or more survival distributions. The generalization to complex samples is based on the characterization of the logrank test as the score test in a Cox model, Under simple random sampling with replacement, this function with rho=0 and gamma=0 is almost identical to the robust score test in the survival package.

Usage

svylogrank(formula, design, rho=0,gamma=0,method=c("small","large","score"), ...)

Arguments

formula

Model formula with a single predictor and optionally a strata term. The predictor must be a factor if it has more than two levels.

design

A survey design object

rho,gamma

Coefficients for the Harrington/Fleming G-rho-gamma tests. The default is the logrank test, rho=1 gives a generalised Wilcoxon test

method

"small" works faster when a matrix with dimension number of events by number of people fits easily in memory; "large" works faster for large data sets; "score" works by brute-force construction of an expanded data set, and is for debugging

...

for future expansion.

Value

A vector containing the z-statistic for comparing each level of the variable to the lowest, the chisquared statistic for the logrank test, and the p-value.

References

Rader K (2014)

Examples

Run this code

library("survival")
data(nwtco)
## stratified on case status
dcchs<-twophase(id=list(~seqno,~seqno), strata=list(NULL,~rel),
         subset=~I(in.subcohort | rel), data=nwtco, method="simple")
svylogrank(Surv(edrel,rel)~factor(stage),design=dcchs)

data(pbc, package="survival")
pbc$randomized <- with(pbc, !is.na(trt) & trt>0)
biasmodel<-glm(randomized~age*edema,data=pbc)
pbc$randprob<-fitted(biasmodel)
dpbc<-svydesign(id=~1, prob=~randprob, strata=~edema, data=subset(pbc,randomized))

svylogrank(Surv(time,status==2)~trt,design=dpbc)

svylogrank(Surv(time,status==2)~trt,design=dpbc,rho=1)

rpbc<-as.svrepdesign(dpbc)
svylogrank(Surv(time,status==2)~trt,design=rpbc)