svysurvreg: Fit accelerated failure models to survey data

In survey: Analysis of Complex Survey Samples

Fit accelerated failure models to survey data

Description

This function calls survreg from the 'survival' package to fit accelerated failure (accelerated life) models to complex survey data, and then computes correct standard errors by linearisation. It has the same arguments as survreg, except that the second argument is design rather than data.

Usage

## S3 method for class 'survey.design'
svysurvreg(formula, design, weights=NULL, subset=NULL, ...)

Arguments

formula	Model formula
design	Survey design object, including two-phase designs
weights	Additional weights to multiply by the sampling weights. No, I don't know why you'd want to do that.
subset	subset to use in fitting (if needed)
...	Other arguments of survreg

Value

Object of class svysurvreg, with the same structure as a survreg object but with NA for the loglikelihood.

Note

The residuals method is identical to that for survreg objects except the weighted option defaults to TRUE

Examples

 data(pbc, package="survival")
 pbc$randomized <- with(pbc, !is.na(trt) & trt>0)
 biasmodel<-glm(randomized~age*edema,data=pbc)
 pbc$randprob<-fitted(biasmodel)
 dpbc<-svydesign(id=~1, prob=~randprob, strata=~edema,
    data=subset(pbc,randomized))

 model <- svysurvreg(Surv(time, status>0)~bili+protime+albumin, design=dpbc, dist="weibull")
summary(model)

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