predict.prodlim: Predicting event probabilities from product limit estimates

Description

Evaluation of estimated survival or event probabilities at given times and covariate constellations.

Usage

## S3 method for class 'prodlim':
predict(object,
		times,
		newdata,
		level.chaos=1,
		type=c("surv","cuminc","list"),
		mode="list",
		bytime=FALSE,
		cause=1,
		...)

Arguments

object

A fitted object of class "prodlim".

times

Vector of times at which to return the estimated probabilities.

newdata

A data frame with the same variable names as those that appear on the right hand side of the 'prodlim' formula. If there are covariates this argument is required.

level.chaos

Integer specifying the sorting of the output: `0' sort by time and newdata; `1' only by time; `2' no sorting at all

type

Choice between "surv","cuminc","list":

"surv": predict survival probabilities only survival models

"cuminc": predict cumulative incidences only competing risk models

"list": find the indices corresponding to times and

mode

Only for type=="surv" and type=="cuminc". Can either be "list" or "matrix". For "matrix" the predicted probabilities will be returned in matrix form.

bytime

Logical. If TRUE and mode=="matrix" the matrix with predicted probabilities will have a column for each time and a row for each newdata. Only when object$covariate.type>1 and more than one time is given.

cause

The cause for predicting the cause-specific cumulative incidence function in competing risk models.

...

Only for compatibility reasons.

Value

type=="surv": A list or a matrix with survival probabilities for all times and all newdata.
type=="cuminc": A list or a matrix with cumulative incidences for all times and all newdata.
type=="list": A list with the following components:
timesThe argument times carried forward
predictorsThe relevant part of the argument newdata.
indicesA list with the following components: time{Where to find values corresponding to the requested times} strata{Where to find values corresponding to the values of the variables in newdata. Together time and strata show where to find the predicted probabilities.}
dimensionsa list with the following components: time{The length of times } strata{ The number of rows in newdata }
names.strataLabels for the covariate values.

Details

Predicted (survival) probabilities are returned that can be plotted, summarized and used for inverse of probability of censoring weighting.

Examples

Run this code

library(survival)
data(pbc)
fit <- prodlim(Hist(time,status)~1,data=pbc)

## predict the survival probs at selected times 
predict(fit,times=c(10,100,1000))

## works also outside the usual range of the Kaplan-Meier
predict(fit,times=c(-1,0,10,100,1000,10000))

## newdata is required if there are strata
## or neighborhoods (ie overlapping strata)
mfit <- prodlim(Hist(time,status)~edema+age,data=pbc)
predict(mfit,times=c(-1,0,10,100,1000,10000),newdata=pbc[18:21,])

## this can be requested in matrix form
predict(mfit,times=c(-1,0,10,100,1000,10000),newdata=pbc[18:21,],mode="matrix")

## and even transposed
predict(mfit,times=c(-1,0,10,100,1000,10000),newdata=pbc[18:21,],mode="matrix",bytime=TRUE)