| svycoxph {survey} | R Documentation |
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 |
survey.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
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$randomized<-with(pbc, !is.na(trt) & trt>0)
biasmodel<-glm(randomized~age*edema,data=pbc,family=binomial)
pbc$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))