riskRegression: IPA – R documentation

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IPA

Explained variation for settings with binary, survival and competing risk outcome

Description

Index of Prediction Accuracy: General R^2 for binary outcome and right censored time to event (survival) outcome also with competing risks

Usage

rsquared(object,...)
IPA(object,...)
## Default S3 method:
rsquared(object,formula,newdata,times,cause,...)
## S3 method for class 'glm'
rsquared(object,formula,newdata,...)
## S3 method for class 'coxph'
rsquared(object,formula,newdata,times,...)
## S3 method for class 'CauseSpecificCox'
rsquared(object,formula,newdata,times,cause,...)
## Default S3 method:
IPA(object,formula,newdata,times,cause,...)
## S3 method for class 'glm'
IPA(object,formula,newdata,...)
## S3 method for class 'coxph'
IPA(object,formula,newdata,times,...)
## S3 method for class 'CauseSpecificCox'
IPA(object,formula,newdata,times,cause,...)

Arguments

`object`	Model for which we want IPA.
`...`	passed to `riskRegression::Score`
`newdata`	Optional validation data set in which to compute IPA
`formula`	Formula passed to `Score`. If not provided, try to use the formula of the call of `object`, if any.
`cause`	For competing risk models the event of interest
`times`	Vector of time points used as prediction horizon for the computation of Brier scores.

Details

IPA (R^2) is calculated based on the model's predicted risks. The Brier score of the model is compared to the Brier score of the null model.

Value

Data frame with explained variation values for the full model.

Author(s)

Thomas A. Gerds <tag@biostat.ku.dk>

Examples

library(prodlim)
library(data.table)
# binary outcome
library(lava)
set.seed(18)
learndat <- sampleData(48,outcome="binary")
lr1 = glm(Y~X1+X2+X7+X9,data=learndat,family=binomial)
IPA(lr1)

## validation data
valdat=sampleData(94,outcome="binary")
IPA(lr1,newdata=valdat)

## predicted risks externally given
p1=predictRisk(lr1,newdata=valdat)
IPA(p1,formula=Y~1,valdat)

# survival
library(survival)
data(pbc)
pbc=na.omit(pbc)
pbctest=(1:NROW(pbc)) %in% sample(1:NROW(pbc),size=.632*NROW(pbc))
pbclearn=pbc[pbctest,]
cox1= coxph(Surv(time,status!=0)~age+sex+log(bili)+log(albumin)+log(protime),
      data=pbclearn,x=TRUE)

## same data
IPA(cox1,formula=Surv(time,status!=0)~1,times=1000)

## validation data
pbcval=pbc[!pbctest,]
IPA(cox1,formula=Surv(time,status!=0)~1,newdata=pbcval,times=1000)

## predicted risks externally given
p2=predictRisk(cox1,newdata=pbcval,times=1000)
IPA(cox1,formula=Surv(time,status!=0)~1,newdata=pbcval,times=1000)
 
# competing risks
data(Melanoma)
Melanomatest=(1:NROW(Melanoma)) %in% sample(1:NROW(Melanoma),size=.632*NROW(Melanoma))
Melanomalearn=Melanoma[Melanomatest,]
fit1 <- CSC(list(Hist(time,status)~sex,
                 Hist(time,status)~invasion+epicel+age),
                 data=Melanoma)
IPA(fit1,times=1000,cause=2)

## validation data
Melanomaval=Melanoma[!Melanomatest,]
IPA(fit1,formula=Hist(time,status)~1,newdata=Melanomaval,times=1000)

## predicted risks externally given
p3= predictRisk(fit1,cause=1,newdata=Melanomaval,times=1000)
IPA(p3,formula=Hist(time,status)~1,cause=1,newdata=Melanomaval,times=1000)

riskRegression

Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

v2020.12.08

GPL (>= 2)

Authors

Thomas Alexander Gerds [aut, cre], Paul Blanche [ctb], Rikke Mortensen [ctb], Marvin Wright [ctb], Nikolaj Tollenaar [ctb], John Muschelli [ctb], Ulla Brasch Mogensen [ctb], Brice Ozenne [aut] (<https://orcid.org/0000-0001-9694-2956>)

Initial release

IPA