pec (version 2.4.9)

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.

Examples

## Not run: 
# library(riskRegression)
# library(prodlim)
# library(lava)
# 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)