Function to extract predicted mean times from various modeling approaches.
# S3 method for aalen
predictRestrictedMeanTime(object,newdata,times,...)
# S3 method for riskRegression
predictRestrictedMeanTime(object,newdata,times,...)
# S3 method for cox.aalen
predictRestrictedMeanTime(object,newdata,times,...)
# S3 method for cph
predictRestrictedMeanTime(object,newdata,times,...)
# S3 method for coxph
predictRestrictedMeanTime(object,newdata,times,...)
# S3 method for matrix
predictRestrictedMeanTime(object,newdata,times,...)
# S3 method for selectCox
predictRestrictedMeanTime(object,newdata,times,...)
# S3 method for prodlim
predictRestrictedMeanTime(object,newdata,times,...)
# S3 method for psm
predictRestrictedMeanTime(object,newdata,times,...)
# S3 method for survfit
predictRestrictedMeanTime(object,newdata,times,...)
# S3 method for pecRpart
predictRestrictedMeanTime(object,newdata,times,...)
#' \method{predictRestrictedMeanTime}{pecCtree}(object,newdata,times,...)A fitted model from which to extract predicted survival probabilities
A data frame containing predictor variable combinations for which to compute predicted survival probabilities.
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.
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.
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.
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. URL https://www.jstatsoft.org/v50/i11.
# NOT RUN {
# generate some survival data
library(prodlim)
set.seed(100)
d <- SimSurv(100)
# then fit a Cox model
library(rms)
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
# the same can be done e.g. for a randomSurvivalForest model
library(randomForestSRC)
rsfmodel <- rfsrc(Surv(time,status)~X1+X2,data=d)
predictRestrictedMeanTime(rsfmodel,newdata=ndat,times=ttt)
# }
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