| cuminc {lcmm} | R Documentation |
Predicted cumulative incidence of event according to a profile of covariates
Description
This function computes the predicted cumulative incidence of each cause of event according to a profile of covariates from a joint latent class model. Confidence bands can be computed by a Monte-Carlo method.
Usage
cuminc(x, time, draws = FALSE, ndraws = 2000, integrateOptions = NULL, ...)
Arguments
x |
an object inheriting from class |
time |
a vector of times at which the cumulative incidence is calculated |
draws |
optional boolean specifying whether a Monte Carlo approximation of the posterior distribution of the cumulative incidence is computed and the median, 2.5% and 97.5% percentiles are given. Otherwise, the predicted cumulative incidence is computed at the point estimate. By default, draws=FALSE. |
ndraws |
if draws=TRUE, ndraws specifies the number of draws that should be generated to approximate the posterior distribution of the predicted cumulative incidence. By default, ndraws=2000. |
integrateOptions |
optional list specifying the subdivisions, rel.tol and stop.on.error options (see ?integrate). |
... |
further arguments, in particular values of the covariates specified in the survival part of the joint model. |
Value
An object of class cuminc containing as many matrices as
profiles defined by the covariates values. Each of these matrices contains
the event-specific cumulative incidences in each latent class at the
different times specified.
Author(s)
Viviane Philipps and Cecile Proust-Lima
See Also
Jointlcmm, plot.Jointlcmm, plot.cuminc
Examples
m2 <- Jointlcmm(fixed= Ydep1~Time*X1,mixture=~Time,random=~Time,
classmb=~X3,subject='ID',survival = Surv(Tevent,Event)~X1+mixture(X2),
hazard="3-quant-splines",hazardtype="PH",ng=2,data=data_lcmm,
B=c(0.64,-0.62,0,0,0.52,0.81,0.41,0.78,0.1,0.77,-0.05,10.43,11.3,-2.6,
-0.52,1.41,-0.05,0.91,0.05,0.21,1.5))
par(mfrow=c(1,2))
plot(cuminc(m2,time=seq(0,20),X1=0,X2=0), ylim=c(0,1))
plot(cuminc(m2,time=seq(0,20),X1=0,X2=1), ylim=c(0,1))