pseudo (version 1.4.3)

pseudoci: Pseudo-observations for the cumulative incidence function

Description

Computes pseudo-observations for modeling competing risks based on the cumulative incidence function.

Usage

pseudoci(time,event, tmax)

Arguments

time

the follow up time.

event

the cause indicator, use 0 as censoring code and integers to name the other causes.

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.

cause

The ordered codes for different causes.

pseudo

A list of matrices - a matrix for each of the causes, ordered by codes. Each row of a matrix 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 cumulative incidence function for each individual and each risk at all the required time points. 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

pseudoyl, pseudomean, pseudosurv

Examples

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

#calculate the pseudo-observations
cutoffs <- c(50,105,170,280,530)
bmt$icr <- bmt$d1 +  bmt$d3
pseudo <- pseudoci(time=bmt$t2,event=bmt$icr,tmax=cutoffs)

#rearrange the data into a long data set, use only pseudo-observations for relapse (icr=2)
b <- NULL
for(it in 1:length(pseudo$time)){
	b <- rbind(b,cbind(bmt,pseudo = pseudo$pseudo[[2]][,it],
	     tpseudo = pseudo$time[it],id=1:nrow(bmt)))
}
b <- b[order(b$id),]


# fit the model
library(geepack)
fit <- geese(pseudo ~ as.factor(tpseudo) + as.factor(group) + as.factor(z8) +
	z1 - 1, data =b, id=id, jack = TRUE, scale.fix=TRUE, family=gaussian,
	mean.link = "cloglog", corstr="independence")

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

# }