tteICE (version 1.1.1)

predict.tteICE: Predict method for 'tteICE' objects at specific time points

Description

This function predicts the potential cumulative incidence function and treatment effect at specific time points.

Usage

# S3 method for tteICE
predict(object, timeset = NULL, ...)

Value

A matrix with each row being time points, potential cumulative incidences (under treated and under control), treatment effects, standard errors, and P-values.

predict a tteICE object. The meanings of each row are: time points, potential cumulative incidences (under treated and under control), treatment effects, standard errors, and P-values.

Arguments

object

A fitted object returned by the function tteICE, surv.tteICE, or scr.tteICE.

timeset

Time at which to predict the risk. If timeset=NULL, risks will be predict at the quartiles of the maximum follow-up time.

...

Other arguments in function predict

See Also

scr.tteICE, surv.tteICE, tteICE surv.boot

surv.tteICE, scr.tteICE, tteICE

Examples

## load data
data(bmt)
bmt = transform(bmt, d4=d2+d3)
A = as.numeric(bmt$group>1)
bmt$A = A
X = as.matrix(bmt[,c('z1','z3','z5')])

## predict results at specified time points
## model fitting using semicompeting risk data
fit1 = scr.tteICE(A, bmt$t1, bmt$d1, bmt$t2, bmt$d2, "composite")
predict(fit1, timeset=c(670,2000))

## predict results without specifying any time points
## model fitting using competing risk data
fit2 = surv.tteICE(A, bmt$t2, bmt$d4, "composite")
predict(fit2)

## a simpler way
library(survival)
fit3 = tteICE(Surv(t2, d4, type = "mstate")~A|z1+z3+z5,
              data=bmt, strategy="composite", method='eff')
predict(fit3, timeset=c(670,2000))
predict(fit3)