survey (version 3.12)

svycoxph: Survey-weighted Cox models.

Description

Fit a proportional hazards model to data from a complex survey design, with inverse-probability weighting and with standard errors corrected for cluster sampling.

Usage

svycoxph(formula, design,subset=NULL, ...)

Arguments

formula

Model formula. Any cluster() terms will be ignored.

design

survey.design object. Must contain all variables in the formula

subset

Expression to select a subpopulation

...

Other arguments passed to coxph.

Value

• An object of class svycoxph.

Details

The main difference between this function and the robust=TRUE option to coxph in the survival package is that this function accounts for the reduction in variance from stratified sampling and the increase in variance from having only a small number of clusters.

Note that strata terms in the model formula describe subsets that have a separate baseline hazard function and need not have anything to do with the stratification of the sampling.

References

Binder DA. (1992) Fitting Cox's proportional hazards models from survey data. Biometrika 79: 139-147

See Also

svydesign, coxph, svyCprod

Examples

## Somewhat unrealistic example of nonresponse bias.
data(pbc, package="survival")

pbc$randomized<-with(pbc, !is.na(trt) & trt>0)
biasmodel<-glm(randomized~age*edema,data=pbc,family=binomial)
pbc$randprob<-fitted(biasmodel)
if (is.null(pbc$albumin)) pbc$albumin<-pbc$alb ##pre2.9.0

dpbc<-svydesign(id=~1, prob=~randprob, strata=~edema, data=subset(pbc,randomized))
rpbc<-as.svrepdesign(dpbc)

svycoxph(Surv(time,status>0)~log(bili)+protime+albumin,design=dpbc)

svycoxph(Surv(time,status>0)~log(bili)+protime+albumin,design=rpbc)