pec: selectFGR – R documentation

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selectFGR

Stepwise variable selection in the Fine & Gray regression competing risk model

Description

This is a wrapper function which first selects variables in the Fine & Gray regression model using crrstep from the crrstep package and then returns a fitted Fine & Gray regression model with the selected variables.

Usage

selectFGR(formula, data, cause = 1, rule = "AIC", direction = "backward", ...)

Arguments

`formula`	A formula whose left hand side is a `Hist` object – see `Hist`. The right hand side specifies (a linear combination of) the covariates. See examples below.
`data`	A data.frame in which all the variables of `formula` can be interpreted.
`cause`	The failure type of interest. Defaults to `1`.
`rule`	Rule to pass on to crrstep ("AIC", "BIC" or "BICcr"), also see `crrstep`
`direction`	see `crrstep`
`...`	Further arguments passed to `crrstep`.

Author(s)

Rob C.M. van Kruijsdijk R.C.M.vanKruijsdijk@umcutrecht.nl

Thomas Alexander Gerds tag@biostat.ku.dk

Examples

## Not run: 
library(riskRegression)
library(prodlim)
library(lava)
if (!requireNamespace("cmprsk",quietly=TRUE)){
library(cmprsk)
library(pec)
m <- crModel()
m <- addvar(m,c('X1','X2','X3','X4','X5','X6','X7','X8','X9','X10'))
distribution(m,c("X2","X7","X9")) <- binomial.lvm()
regression(m,eventtime1~X1+X2+X5+X9) <- c(-1,1,0.5,0.8)
set.seed(100)
d <- sim(m,100)
## full formula
ff <- Hist(time, event) ~ X1 + X2 + X3 + X4 +X5 + X6 + X7+ X8 + X9 + X10

# Fit full model with FGR
fg <- FGR(ff,cause=1,data=d)

# Backward selection based on the AIC
sfgAIC <- selectFGR(ff, data=d, rule="AIC", direction="backward") 

sfgAIC$fit # Final FGR-model with selected variables

# Risk reclassification plot at time = 4
plot(predictEventProb(fg,times=4,newdata=d),
     predictEventProb(sfgAIC,times=4,newdata=d))

# Backward selection based on the BIC, while forcing 
# the last two variables (X9 and X10) in the model
sfgBIC <- selectFGR(ff, data=d, rule="BIC", direction="backward", 
                  scope.min=~X9+X10)

## apparent performance
pec(list(full.model=fg,selectedAIC=sfgAIC,selectedBIC=sfgBIC),
    formula=Hist(time, event)~1,
    data=d)


## bootstrap cross-validation performance
set.seed(7)
pec(list(full.model=fg,selectedAIC=sfgAIC,selectedBIC=sfgBIC),
    formula=Hist(time, event)~1,
    data=d,
    B=5,
    splitMethod="bootcv")
}

## End(Not run)

pec

Prediction Error Curves for Risk Prediction Models in Survival Analysis

v2020.11.17

GPL (>= 2)

Authors

Thomas A. Gerds

Initial release