R: Fit accelerated failure models to survey data

svysurvreg {survey} | R Documentation

## 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)

[Package survey version 3.32-1 Index]