predictRisk: Extrating predicting risks from regression models

Description

Extract event probabilities from fitted regression models and machine learning objects.

Usage

# S3 method for glm
predictRisk(object,newdata,...)
# S3 method for cox.aalen
predictRisk(object,newdata,times,...)
# S3 method for cph
predictRisk(object,newdata,times,...)
# S3 method for coxph
predictRisk(object,newdata,times,...)
# S3 method for matrix
predictRisk(object,newdata,times,cause,...)
# S3 method for selectCox
predictRisk(object,newdata,times,...)
# S3 method for psm
predictRisk(object,newdata,times,...)
# S3 method for survfit
predictRisk(object,newdata,times,...)
# S3 method for riskRegression
predictRisk(object,newdata,times,cause,...)
# S3 method for prodlim
predictRisk(object,newdata,times,cause,...)
# S3 method for rfsrc
predictRisk(object,newdata,times,cause,...)
# S3 method for FGR
predictRisk(object,newdata,times,cause,...)
# S3 method for CauseSpecificCox
predictRisk(object,newdata,times,cause,...)

Arguments

object

A fitted model from which to extract predicted event probabilities

newdata

A data frame containing predictor variable combinations for which to compute predicted event probabilities.

times

A vector of times in the range of the response variable, for which the cumulative incidences event probabilities are computed.

cause

Identifies the cause of interest among the competing events.

…

Additional arguments that are passed on to the current method.

Value

For binary outcome a vector with predicted risks. For survival outcome with and without competing risks a matrix with as many rows as NROW(newdata) and as many columns as length(times). Each entry is a probability and in rows the values should be increasing.

Details

The function predictRisk is a generic function, meaning that it invokes specifically designed functions depending on the 'class' of the first argument.

See predictRisk.

Examples

Run this code

## binary outcome
library(rms)
set.seed(7)
x <- abs(rnorm(20))
d <- data.frame(y=rbinom(20,1,x/max(x)),x=x,z=rnorm(20))
nd <- data.frame(y=rbinom(8,1,x/max(x)),x=abs(rnorm(8)),z=rnorm(8))
fit <- lrm(y~x+z,d)
predictRisk(fit,newdata=nd)

## survival outcome
# generate survival data
library(prodlim)
set.seed(100)
d <- sampleData(100,outcome="survival")
# then fit a Cox model
library(rms)
cphmodel <- cph(Surv(time,event)~X1+X2,data=d,surv=TRUE,x=TRUE,y=TRUE)
# or via survival
library(survival)
coxphmodel <- coxph(Surv(time,event)~X1+X2,data=d,x=TRUE,y=TRUE)

# Extract predicted survival probabilities 
# 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
predictRisk(cphmodel,newdata=ndat,times=ttt)
predictRisk(coxphmodel,newdata=ndat,times=ttt)

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

## simulate learning and validation data
learndat <- sampleData(100,outcome="survival")
valdat <- sampleData(100,outcome="survival")
## use the learning data to fit a Cox model
library(survival)
fitCox <- coxph(Surv(time,event)~X1+X2,data=learndat,x=TRUE,y=TRUE)
## suppose we want to predict the survival probabilities for all subjects
## in the validation data at the following time points:
## 0, 12, 24, 36, 48, 60
psurv <- predictRisk(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

# Do the same for a randomSurvivalForest model
library(randomForestSRC)
rsfmodel <- rfsrc(Surv(time,event)~X1+X2,data=learndat)
prsfsurv=predictRisk(rsfmodel,newdata=valdat,times=seq(0,60,12))
# plot(psurv,prsfsurv)

## Cox with ridge option
f1 <- coxph(Surv(time,event)~X1+X2,data=learndat,x=TRUE,y=TRUE)
f2 <- coxph(Surv(time,event)~ridge(X1)+ridge(X2),data=learndat,x=TRUE,y=TRUE)

plot(predictRisk(f1,newdata=valdat,times=10),
     riskRegression:::predictRisk.coxph(f2,newdata=valdat,times=10),
     xlim=c(0,1),
     ylim=c(0,1),
     xlab="Unpenalized predicted survival chance at 10",
     ylab="Ridge predicted survival chance at 10")


## competing risks

library(survival)
library(riskRegression)
library(prodlim)
train <- SimCompRisk(100)
test <- SimCompRisk(10)
cox.fit  <- CSC(Hist(time,cause)~X1+X2,data=train)
predictRisk(cox.fit,newdata=test,times=seq(1:10),cause=1)

## with strata
cox.fit2  <- CSC(list(Hist(time,cause)~strata(X1)+X2,Hist(time,cause)~X1+X2),data=train)
predictRisk(cox.fit2,newdata=test,times=seq(1:10),cause=1)