predict.mixture.2states: Cumulative Incidence Function Form Horizontal Mixture Model...

In RISCA: Causal Inference and Prediction in Cohort-Based Analyses

Cumulative Incidence Function Form Horizontal Mixture Model With Two Competing Events

Description

This function allows to estimate a cumulative incidence function (CIF) from an horizontal mixture model with two competing events, i.e. the results obtained from the function mixture.2states.

Usage

pred.mixture.2states(model, failure, times, cov.12=NULL, cov.13=NULL, cov.p=NULL)

Arguments

model	A list obtained by using the function mixture.2states.
failure	A numeric value for identifying the event for which the CIF has to be computed. Two possible values are allowed: 2 (for the CIF related to X=2) and 3 (for the CIF related to X=3).
times	A numeric vector with positive values related to the times for which the CIF has to be computed.
cov.12	A vector, matrix or data frame in which to look for variables related to the time from X=1 to X=2 with which to predict the CIF.
cov.13	A vector, matrix or data frame in which to look for variables related to the time from X=1 to X=3 with which to predict the CIF.
cov.p	A vector, matrix or data frame in which to look for variables related to the probability P(X=2).

Details

The covariates has to be identical than the ones included in the mixture model declared in the argument model. More precisely, the columns of cov.12, cov.13 and cov.p must correspond to the same variables.

Value

times	A numeric vector with the times for which the CIF has to be computed.
cif	A matrix with the predicted CIF for the times in columns and the individuals in rows.

Author(s)

Yohann Foucher <[e-mail]>

References

Trebern-Launay et al. Horizontal mixture model for competing risks: a method used in waitlisted renal transplant candidates. European Journal of Epidemiology. 33(3):275-286, 2018. <doi: 10.1007/s10654-017-0322-3>.

Examples

# import the observed data
# X=1 corresponds to initial state with a functioning graft,
# X=2 to acute rejection episode (transient state), 
# X=3 to return to dialysis, X=4 to death with a functioning graft

data(dataDIVAT1)

# A subgroup analysis to reduce the time needed for this example

dataDIVAT1$id<-c(1:nrow(dataDIVAT1))
set.seed(2)
d2<-dataDIVAT1[dataDIVAT1$id %in% sample(dataDIVAT1$id, 300, replace = FALSE),]

# Data-management: two competing events
# the patient death is now X=2
# the return in dialysis is now X=3

d2$time<-NA
d2$time[d2$trajectory==1]<-d2$time1[d2$trajectory==1]

d2$time[d2$trajectory==12]<-d2$time2[d2$trajectory==12]
d2$trajectory[d2$trajectory==12]<-1

d2$time[d2$trajectory==13]<-d2$time1[d2$trajectory==13]

d2$time[d2$trajectory==123]<-d2$time2[d2$trajectory==123]
d2$trajectory[d2$trajectory==123]<-13

d2$time[d2$trajectory==14]<-d2$time1[d2$trajectory==14]

d2$time[d2$trajectory==124]<-d2$time2[d2$trajectory==124]
d2$trajectory[d2$trajectory==124]<-14

d2$trajectory[d2$trajectory==14]<-12

table(d2$trajectory)

# Univariable horizontal mixture model one binary explicative variable
# z is 1 if delayed graft function and 0 otherwise

mm2.model <- mixture.2states(times=d2$time, sequences=d2$trajectory,
  weights=NULL, dist=c("E","W"), cuts.12=NULL, cuts.13=NULL, 
  ini.dist.12=c(9.28), ini.dist.13=c(9.92, -0.23), 
  cov.12=d2$z, init.cov.12=0.84, names.12="beta_12",
  cov.13=d2$z, init.cov.13=0.76, names.13="beta_13",
  cov.p=NULL, init.cov.p=NULL, names.p=NULL, init.intercept.p=-0.75,
  conf.int=TRUE, silent=FALSE)

cif2.mm2 <- pred.mixture.2states(mm2.model, failure=2, times=seq(0, 4000, by=30),
 cov.12=c(0,1), cov.13=c(0,1), cov.p=NULL)

plot(cif2.mm2$times/365.25, cif2.mm2$cif[1,], col = 1, type="l", lty = 1,
 ylim=c(0,1), lwd =2, ylab="Cumulative Incidence Function",
 xlab="Times (years)", main="", xlim=c(0, 11), legend=FALSE)

lines(cif2.mm2$times/365.25, cif2.mm2$cif[2,], lwd=2, col=2)

RISCA documentation built on March 31, 2023, 11:06 p.m.