# Conditional probability function of a competing event: The Cprob package

The Cprob package permits to estimate the conditional probability function of a competing event, and to fit, using the temporal process regression or the pseudo-value approach, a proportional-odds model to the conditional probability function (or other models by specifying another link function).

## Installation

``install.packages("Cprob")``

Or you can install the development version from github

``````## if necessary
## install.packages("devtools")
devtools::install_github("aallignol/Cprob")``````

## Usage

The conditional probability function can be estimated using the `cpf` function.

``````library(Cprob)

mgus\$AGE <- ifelse(mgus\$age < 64, 0, 1)
CP <- cpf(Hist(time, ev)~AGE, data = mgus)
CP
summary(CP)``````

A regression model can be fitted either using temporal process regression

``````fit.cpfpo <- cpfpo(Hist(time, ev)~ age + creat,
data = mgus, tis=seq(10, 30, 0.3),
w=rep(1,67))

## and plot the odds-ratios
if(require("lattice")) {
xyplot(fit.cpfpo, scales = list(relation = "free"), layout = c(3, 1))
}``````

or the pseudo-values approach

``````data(mgus)

cutoffs <- quantile(mgus\$time, probs = seq(0, 1, 0.05))[-1]

## with fancy variance estimation
fit1 <- pseudocpf(Hist(time, ev) ~ age + creat, mgus, id = id,
timepoints = cutoffs, corstr = "independence",
scale.value = TRUE)
summary(fit1)

## with jackknife variance estimation
fit2 <- pseudocpf(Hist(time, ev) ~ age + creat, mgus, id = id,
timepoints = cutoffs, corstr = "independence",
scale.value = TRUE, jack = TRUE)
summary(fit2)
}``````