svycoxph {survey}R Documentation

Survey-weighted Cox models.


Fit a proportional hazards model to data from a complex survey design.


svycoxph(formula, design,subset=NULL, ...)
## S3 method for class 'svycoxph':
predict(object, newdata, se=FALSE,
    type=c("lp", "risk", "expected", "terms","curve"),...)


formula Model formula. Any cluster() terms will be ignored.
design object. Must contain all variables in the formula
subset Expression to select a subpopulation
object A svycoxph object
newdata New data for prediction
se Compute standard errors? This takes a lot of memory for type="curve"
type "curve" does predicted survival curves. The other values are passed to predict.coxph()
... Other arguments passed to coxph.


The main difference between svycoxph 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.

The standard errors for predicted survival curves are available only by linearization, not by replicate weights (at the moment). Use withReplicates to get standard errors with replicate weights. Predicted survival curves are not available for stratified Cox models.

The standard errors use the delta-method approach of Williams (1995) for the Nelson-Aalen estimator, modified to handle the Cox model following Tsiatis (1981). The standard errors agree closely with survfit.coxph for independent sampling when the model fits well, but are larger when the model fits poorly.


An object of class svycoxph for svycoxph, an object of class svykm or svykmlist for predict(,type="curve").


The standard error calculation for survival curves uses memory proportional to the sample size times the square of the number of events.


Thomas Lumley


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

Tsiatis AA (1981) A Large Sample Study of Cox's Regression Model. Annals of Statistics 9(1) 93-108

Williams RL (1995) "Product-Limit Survival Functions with Correlated Survival Times" Lifetime Data Analysis 1: 171–186

See Also

coxph, predict.coxph

svykm for estimation of Kaplan-Meier survival curves and for methods that operate on survival curves.


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

pbc$randomized<-with(pbc, ! & trt>0)
if (is.null(pbc$albumin)) pbc$albumin<-pbc$alb ##pre2.9.0

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



s<-predict(model,se=TRUE, type="curve",
     newdata=data.frame(bili=c(3,9), protime=c(10,10), albumin=c(3.5,3.5)))
lines(s[[2]], ci=TRUE,col="royalblue")
quantile(s[[1]], ci=TRUE)
confint(s[[2]], parm=365*(1:5))

[Package survey version 3.18 Index]