R: Compare survival distributions

svylogrank {survey} | R Documentation

## 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, Kevin Andrew. 2014. Methods for Analyzing Survival and Binary Data in Complex Surveys. Doctoral dissertation, Harvard University.http://nrs.harvard.edu/urn-3:HUL.InstRepos:12274283

### See Also

`svykm`, `svycoxph`.

### Examples

    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)

[Package survey version 3.32-1 Index]