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))

[Package *survey* version 3.18 Index]