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
You can download the stable version on CRAN
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)
}