predictRestrictedMeanTime: Predicting restricted mean time

In pec: Prediction Error Curves for Risk Prediction Models in Survival Analysis

Predicting restricted mean time

Description

Function to extract predicted mean times from various modeling approaches.

Usage

## S3 method for class 'aalen'
predictRestrictedMeanTime(object,newdata,times,...)
## S3 method for class 'riskRegression'
predictRestrictedMeanTime(object,newdata,times,...)
## S3 method for class 'cox.aalen'
predictRestrictedMeanTime(object,newdata,times,...)
## S3 method for class 'cph'
predictRestrictedMeanTime(object,newdata,times,...)
## S3 method for class 'coxph'
predictRestrictedMeanTime(object,newdata,times,...)
## S3 method for class 'matrix'
predictRestrictedMeanTime(object,newdata,times,...)
## S3 method for class 'selectCox'
predictRestrictedMeanTime(object,newdata,times,...)
## S3 method for class 'prodlim'
predictRestrictedMeanTime(object,newdata,times,...)
## S3 method for class 'psm'
predictRestrictedMeanTime(object,newdata,times,...)
## S3 method for class 'survfit'
predictRestrictedMeanTime(object,newdata,times,...)
## S3 method for class 'pecRpart'
predictRestrictedMeanTime(object,newdata,times,...)
#' \method{predictRestrictedMeanTime}{pecCtree}(object,newdata,times,...)

Arguments

object	A fitted model from which to extract predicted survival probabilities
newdata	A data frame containing predictor variable combinations for which to compute predicted survival probabilities.
times	A vector of times in the range of the response variable, e.g. times when the response is a survival object, at which to return the survival probabilities.
...	Additional arguments that are passed on to the current method.

Details

The function predictRestrictedMeanTime is a generic function, meaning that it invokes a different function dependent on the 'class' of the first argument.

See also predictSurvProb.

Value

A matrix with as many rows as NROW(newdata) and as many columns as length(times). Each entry should be a probability and in rows the values should be decreasing.

Note

In order to assess the predictive performance of a new survival model a specific predictRestrictedMeanTime S3 method has to be written. For examples, see the bodies of the existing methods.

The performance of the assessment procedure, in particular for resampling where the model is repeatedly evaluated, will be improved by supressing in the call to the model all the computations that are not needed for probability prediction. For example, se.fit=FALSE can be set in the call to cph.

Author(s)

Thomas A. Gerds [e-mail]

References

Ulla B. Mogensen, Hemant Ishwaran, Thomas A. Gerds (2012). Evaluating Random Forests for Survival Analysis Using Prediction Error Curves. Journal of Statistical Software, 50(11), 1-23. DOI 10.18637/jss.v050.i11

See Also

predict,survfit

Examples

# generate some survival data
library(prodlim)
set.seed(100)
d <- SimSurv(100)
# then fit a Cox model
library(rms)
library(survival)
coxmodel <- cph(Surv(time,status)~X1+X2,data=d,surv=TRUE)

# predicted survival probabilities can be extracted
# at selected time-points:
ttt <- quantile(d$time)
# for selected predictor values:
ndat <- data.frame(X1=c(0.25,0.25,-0.05,0.05),X2=c(0,1,0,1))
# as follows
predictRestrictedMeanTime(coxmodel,newdata=ndat,times=ttt)

# stratified cox model
sfit <- coxph(Surv(time,status)~strata(X1)+X2,data=d,x=TRUE,y=TRUE)
predictRestrictedMeanTime(sfit,newdata=d[1:3,],times=c(1,3,5,10))

## simulate some learning and some validation data
learndat <- SimSurv(100)
valdat <- SimSurv(100)
## use the learning data to fit a Cox model
library(survival)
fitCox <- coxph(Surv(time,status)~X1+X2,data=learndat,x=TRUE,y=TRUE)
## suppose we want to predict the survival probabilities for all patients
## in the validation data at the following time points:
## 0, 12, 24, 36, 48, 60
psurv <- predictRestrictedMeanTime(fitCox,newdata=valdat,times=seq(0,60,12))
## This is a matrix with survival probabilities
## one column for each of the 5 time points
## one row for each validation set individual

pec documentation built on April 11, 2023, 5:55 p.m.