`svysurvreg.Rd`

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`

.

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

- 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`

Object of class `svysurvreg`

, with the same structure as a `survreg`

object but with `NA`

for the loglikelihood.

The `residuals`

method is identical to that for `survreg`

objects except the `weighted`

option defaults to `TRUE`

```
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)
#>
#> Call:
#> svysurvreg(formula = Surv(time, status > 0) ~ bili + protime +
#> albumin, design = dpbc, dist = "weibull")
#> Value Std. Error z p
#> (Intercept) 7.33162 0.77621 9.45 < 2e-16
#> bili -0.07817 0.00879 -8.90 < 2e-16
#> protime -0.17644 0.05971 -2.96 0.0031
#> albumin 0.85167 0.14232 5.98 2.2e-09
#> Log(scale) -0.42104 0.06142 -6.86 7.1e-12
#>
#> Scale= 0.656
#>
#> Weibull distribution
#> Loglik(model)= NA Loglik(intercept only)= NA
#> Chisq= on 3 degrees of freedom, p=
#> Number of Newton-Raphson Iterations: 6
#> n= 312
#>
```