Description Usage Arguments Value References See Also Examples
Computes a weighted version of the logrank test 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. The rho=0
and gamma=0
version was
proposed by Rader (2014).
1  svylogrank(formula, design, rho=0,gamma=0,method=c("small","large","score"), ...)

formula 
Model formula with a single predictor. 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 Grhogamma
tests. The default is the logrank test, 
method 

... 
for future expansion. 
A vector containing the zstatistic for comparing each level of the variable to the lowest, the chisquared statistic for the logrank test, and the pvalue.
Rader, Kevin Andrew. 2014. Methods for Analyzing Survival and Binary Data in Complex Surveys. Doctoral dissertation, Harvard University.http://nrs.harvard.edu/urn3:HUL.InstRepos:12274283
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  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)

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