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.

…

Additional arguments that are passed on to the current method.

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.

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

# NOT RUN {
## 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)
# }
# NOT RUN {
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")
# }
# NOT RUN {
## 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)

# }