svycoxph: Survey-weighted Cox models.

In survey: Analysis of Complex Survey Samples

Survey-weighted Cox models.

Description

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

Usage

svycoxph(formula, design,subset=NULL, rescale=TRUE, ...)
## S3 method for class 'svycoxph'
predict(object, newdata, se=FALSE,
    type=c("lp", "risk", "terms","curve"),...)
## S3 method for class 'svycoxph'
AIC(object, ..., k = 2)    

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
rescale	Rescale weights to improve numerical stability
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()
...	For AIC, more models to compare the AIC of. For svycoxph, other arguments passed to coxph.
k	The penalty per parameter that would be used under independent sampling: AIC has k=2

Details

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 AIC method uses the same approach as AIC.svyglm, though the relevance of the criterion this optimises is a bit less clear than for generalised linear models.

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. I believe the standard errors are equivalent to those of Lin (2000), but I don't know of any implementation that would allow a check.

Value

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

Warning

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

Author(s)

Thomas Lumley

References

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

Lin D-Y (2000) On fitting Cox's proportional hazards model to survey data. Biometrika 87: 37-47

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.

regTermTest for Wald and (Rao-Scott) likelihood ratio tests for one or more parameters.

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)

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

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

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

survey documentation built on May 29, 2024, 12:05 p.m.