svykm  R Documentation 
Estimates the survival function using a weighted KaplanMeier estimator.
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,...)
formula 
Twosided formula. The response variable should be a rightcensored

design 
survey design object 
se 
Compute standard errors? This is slow for moderate to large data sets 
... 
in 
x 
a 
xlab,ylab,ylim,type 
as for 
lty 
Line type, see 
ci 
Plot (or return, for 
pars 
A list of vectors of graphical parameters for the
separate curves in a 
object 
A 
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

When standard errors are computed, the survival curve is actually the Aalen (hazardbased) estimator rather than the KaplanMeier 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 equalprobability 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.
For svykm
, an object of class svykm
for a single curve or svykmlist
for multiple curves.
Link, C. L. (1984). Confidence intervals for the survival function using Cox's proportional hazards model with covariates. Biometrics 40, 601610.
Williams RL (1995) "ProductLimit 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, 622627.
predict.svycoxph
for survival curves from a Cox model
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)
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