R: Predicting event probabilities (cumulative incidences) in...

predictEventProb {pec} | R Documentation

## Predicting event probabilities (cumulative incidences) in competing risk models.

### Description

Function to extract event probability predictions from various modeling approaches. The most prominent one is the combination of cause-specific Cox regression models which can be fitted with the function `cumincCox` from the package `compRisk`.

### Usage

    predictEventProb(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.

### Details

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

See `predictSurvProb`.

### Value

A matrix with as many rows as `NROW(newdata)` and as many columns as `length(times)`. Each entry should be a probability and in rows the values should be increasing.

### Author(s)

Thomas A. Gerds tag@biostat.ku.dk

### See Also

See `predictSurvProb`.

### Examples

    library(pec)
    library(survival)
    library(riskRegression)
    library(prodlim)
    train <- SimCompRisk(100)
    test <- SimCompRisk(10)
    cox.fit  <- CSC(Hist(time,cause)~X1+X2,data=train)
    predictEventProb(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)
    predictEventProb(cox.fit2,newdata=test,times=seq(1:10),cause=1)

[Package pec version 2020.11.17 Index]