pseudosurv {pseudo} | R Documentation |
Computes pseudo-observations for modeling survival function based on the Kaplan-Meier estimator.
pseudosurv(time,event, tmax)
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. |
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.
A data frame. The first two columns contain the follow up time and the status indicator as given by the user. The following columns present the pseudo-observations for each of the required (sorted) time points.
Klein J.P., Gerster M., Andersen P.K., Tarima S.: "SAS and R Functions to Compute Pseudo-values for Censored Data Regression." Department of Biostatistics, University of Copenhagen, research report 07/11.
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 b <- NULL for(j in 3:ncol(pseudo)){ b <- rbind(b,cbind(bmt,pseudo=pseudo[,j],tpseudo=cutoffs[j-2], 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)