svycoxph {survey}R Documentation

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.

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.

Value

An object of class svycoxph.

Author(s)

Thomas Lumley

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)

[Package survey version 3.13 Index]