# Generate illness-death model objects Function to generate an illness-death model for simulation. ```r idmModel( scale.illtime = 1/100, shape.illtime = 1, scale.lifetime = 1/100, shape.lifetime = 1, scale.waittime = 1/100, shape.waittime = 1, scale.censtime = 1/100, shape.censtime = 1, n.inspections = 5, schedule = 10, punctuality = 5 ) ``` ## Arguments - `scale.illtime`: Weilbull scale for latent illness time - `shape.illtime`: Weilbull shape for latent illness time - `scale.lifetime`: Weilbull scale for latent life time - `shape.lifetime`: Weilbull shape for latent life time - `scale.waittime`: Weilbull scale for latent life time - `shape.waittime`: Weilbull shape for latent life time - `scale.censtime`: Weilbull scale for censoring time - `shape.censtime`: Weilbull shape for censoring time - `n.inspections`: Number of inspection times - `schedule`: Mean of the waiting time between adjacent inspections. - `punctuality`: Standard deviation of waiting time between inspections. ## Returns A latent variable model object `lvm` ## Details Based on the functionality of the lava PACKAGE the function generates a latent variable model (latent illtime, waittime and lifetime) and censoring mechanism (censtime, inspection1,inspection2,...,inspectionK). The function `sim.idmModel` then simulates right censored lifetimes and interval censored illness times. ## Examples ```r library(lava) library(prodlim) # generate illness-death model based on exponentially # distributed times m <- idmModel(scale.illtime=1/70, shape.illtime=1.8, scale.lifetime=1/50, shape.lifetime=0.7, scale.waittime=1/30, shape.waittime=0.7) round(sim(m,6),1) # Estimate the parameters of the Weibull models # based on the uncensored exact event times # and the uncensored illstatus. set.seed(18) d <- sim(m,100,latent=FALSE) d$uncensored.status <- 1 f <- idm(formula01=Hist(time=illtime,event=illstatus)~1, formula02=Hist(time=lifetime,event=uncensored.status)~1, data=d, conf.int=FALSE) print(f) # Change the rate of the 0->2 and 0->1 transitions # also the rate of the 1->2 transition # and also lower the censoring rate m <- idmModel(scale.lifetime=1/2000, scale.waittime=1/30, scale.illtime=1/1000, scale.censtime=1/1000) set.seed(18) d <- sim(m,50,latent=TRUE) d$uncensored.status <- 1 f <- idm(formula01=Hist(time=observed.illtime,event=illstatus)~1, formula02=Hist(time=observed.lifetime,event=uncensored.status)~1, data=d, conf.int=FALSE) print(f) # Estimate based on the right censored observations fc <- idm(formula01=Hist(time=illtime,event=seen.ill)~1, formula02=Hist(time=observed.lifetime,event=seen.exit)~1, data=d, conf.int=FALSE) print(fc) # Estimate based on interval censored and right censored observations fi <- idm(formula01=Hist(time=list(L,R),event=seen.ill)~1, formula02=Hist(time=observed.lifetime,event=seen.exit)~1, data=d, conf.int=FALSE) print(fi) # Estimation of covariate effects: # X1, X2, X3 m <- idmModel(shape.waittime=2, scale.lifetime=1/2000, scale.waittime=1/300, scale.illtime=1/10000, scale.censtime=1/10000) distribution(m,"X1") <- binomial.lvm(p=0.3) distribution(m,"X2") <- normal.lvm(mean=120,sd=20) distribution(m,"X3") <- normal.lvm(mean=50,sd=20) regression(m,to="latent.illtime",from="X1") <- 1.7 regression(m,to="latent.illtime",from="X2") <- 0.07 regression(m,to="latent.illtime",from="X3") <- -0.1 regression(m,to="latent.waittime",from="X1") <- 1.8 regression(m,to="latent.lifetime",from="X1") <- 0.7 set.seed(28) d <- sim(m,100,latent=TRUE) head(d) table(ill=d$seen.ill,death=d$seen.exit) # Estimation based on uncensored data d$uncensored.status <- 1 # uncensored data F1 <- idm(formula01=Hist(time=illtime,event=illstatus)~X1+X2+X3, formula02=Hist(time=lifetime,event=uncensored.status)~X1+X2+X3, data=d,conf.int=FALSE) print(F1) # Estimation based on right censored data F2 <- idm(formula01=Hist(time=illtime,event=seen.ill)~X1+X2+X3, formula02=Hist(time=observed.lifetime,event=seen.exit)~X1+X2+X3, data=d,conf.int=FALSE) print(F2) # Estimation based on interval censored and right censored data F3 <- idm(formula01=Hist(time=list(L,R),event=seen.ill)~X1+X2+X3, formula02=Hist(time=observed.lifetime,event=seen.exit)~X1+X2+X3, data=d,conf.int=FALSE) print(F3) cbind(uncensored=F1$coef,right.censored=F2$coef,interval.censored=F3$coef) ``` ## Author(s) Thomas Alexander Gerds

idmModel function