R: Estimate survival function.

svykm {survey} | R Documentation

## Estimate survival function.

### Description

Estimates the survival function using a weighted Kaplan-Meier estimator.

### Usage

    svykm(formula, design,se=FALSE, ...)
    ## S3 method for class 'svykm'
    plot(x,xlab="time",ylab="Proportion surviving",
      ylim=c(0,1),ci=NULL,lty=1,...)
    ## S3 method for class 'svykm'
    lines(x,xlab="time",type="s",ci=FALSE,lty=1,...)
    ## S3 method for class 'svykmlist'
    plot(x, pars=NULL, ci=FALSE,...)
    ## S3 method for class 'svykm'
    quantile(x, probs=c(0.75,0.5,0.25),ci=FALSE,level=0.95,...)
    ## S3 method for class 'svykm'
    confint(object,parm,level=0.95,...)

### Arguments

`formula` | Two-sided formula. The response variable should be a right-censored `Surv` object
`design` | survey design object
`se` | Compute standard errors? This is slow for moderate to large data sets
`...` | in `plot` and `lines` methods, graphical parameters
`x` | a `svykm` or `svykmlist` object
`xlab,ylab,ylim,type` | as for `plot`
`lty` | Line type, see `par`
`ci` | Plot (or return, for`quantile`) the confidence interval
`pars` | A list of vectors of graphical parameters for the separate curves in a `svykmlist` object
`object` | A `svykm` object
`parm` | vector of times to report confidence intervals
`level` | confidence level
`probs` | survival probabilities for computing survival quantiles (note that these are the complement of the usual `quantile` input, so 0.9 means 90% surviving, not 90% dead)

### Details

When standard errors are computed, the survival curve is actually the Aalen (hazard-based) estimator rather than the Kaplan-Meier estimator.

The standard error computations use memory proportional to the sample size times the square of the number of events. This can be a lot.

In the case of equal-probability cluster sampling without replacement the computations are essentially the same as those of Williams (1995), and the same linearization strategy is used for other designs.

Confidence intervals are computed on the log(survival) scale, following the default in `survival` package, which was based on simulations by Link(1984).

Confidence intervals for quantiles use Woodruff's method: the interval is the intersection of the horizontal line at the specified quantile with the pointwise confidence band around the survival curve.

### Value

For `svykm`, an object of class `svykm` for a single curve or `svykmlist` for multiple curves.

### References

Link, C. L. (1984). Confidence intervals for the survival function using Cox's proportional hazards model with covariates. Biometrics 40, 601-610.

Williams RL (1995) "Product-Limit Survival Functions with Correlated Survival Times" Lifetime Data Analysis 1: 171–186

Woodruff RS (1952) Confidence intervals for medians and other position measures. JASA 57, 622-627.

### See Also

`predict.svycoxph` for survival curves from a Cox model

### 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))

    s1<-svykm(Surv(time,status>0)~1, design=dpbc)
    s2<-svykm(Surv(time,status>0)~I(bili>6), design=dpbc)

    plot(s1)
    plot(s2)
    plot(s2, lwd=2, pars=list(lty=c(1,2),col=c("purple","forestgreen")))

    quantile(s1, probs=c(0.9,0.75,0.5,0.25,0.1))

    s3<-svykm(Surv(time,status>0)~I(bili>6), design=dpbc,se=TRUE)
    plot(s3[[2]],col="purple")

    confint(s3[[2]], parm=365*(1:5))
    quantile(s3[[1]], ci=TRUE)

[Package survey version 3.32-1 Index]