pseudo (version 1.4.3)

pseudosurv: Pseudo-observations for the Kaplan-Meier estimate

Description

Computes pseudo-observations for modeling survival function based on the Kaplan-Meier estimator.

Usage

pseudosurv(time,event, tmax)

Arguments

time

the follow up time.

event

the status indicator: 0=alive, 1=dead.

tmax

a vector of time points at which the pseudo-observations are to be computed. If missing, the pseudo-observations are reported at each event time.

Value

A list containing the following objects:

time

The ordered time points at which the pseudo-observations are evaluated.

pseudo

A matrix. Each row belongs to one individual (ordered as in the original data set), each column presents a time point (ordered in time).

Details

The function calculates the pseudo-observations for the value of the survival function at prespecified time-points for each individual. The pseudo-observations can be used for fitting a regression model with a generalized estimating equation. No missing values in either time or event vector are allowed.

Please note that the output of the function has changed and the usage is thus no longer the same as in the reference paper - the new usage is described in the example below. Similar (faster) version of the function is available in the R-package prodlim (jackknife).

References

Klein J.P., Gerster M., Andersen P.K., Tarima S., POHAR PERME, M.: "SAS and R Functions to Compute Pseudo-values for Censored Data Regression." Comput. methods programs biomed., 2008, 89 (3): 289-300

See Also

pseudomean, pseudoci, pseudoyl

Examples

# NOT RUN {
library(KMsurv)
data(bmt)

#calculate the pseudo-observations
cutoffs <- c(50,105,170,280,530)
pseudo <- pseudosurv(time=bmt$t2,event=bmt$d3,tmax=cutoffs)


#rearrange the data into a long data set
b <- NULL
for(it in 1:length(pseudo$time)){
	b <- rbind(b,cbind(bmt,pseudo = pseudo$pseudo[,it],
	     tpseudo = pseudo$time[it],id=1:nrow(bmt)))
}
b <- b[order(b$id),]



#fit a Cox model using GEE
library(geepack)
summary(fit <- geese(pseudo~as.factor(tpseudo)+as.factor(group)+
        as.factor(z8)+z1,data=b,scale.fix=TRUE,family=gaussian,
        jack=TRUE, mean.link="cloglog",corstr="independence"))

#The results using the AJ variance estimate
round(cbind(mean = fit$beta,SD = sqrt(diag(fit$vbeta.ajs)),
	Z = fit$beta/sqrt(diag(fit$vbeta.ajs)),	PVal =
	2-2*pnorm(abs(fit$beta/sqrt(diag(fit$vbeta.ajs))))),4)
# }