# Cumulative Incidence Function Form Horizontal Mixture Model With Two Competing Events 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`. ```r 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. ## Returns - **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. ## 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. . ## Author(s) Yohann Foucher ## Examples ```r # 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) ```

predict.mixture.2states function