SmoothHazard (version 2024.04.10)

idmModel: Generate illness-death model objects

Description

Function to generate an illness-death model for simulation.

Usage

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
)

Value

A latent variable model object lvm

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.

Author

Thomas Alexander Gerds

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

# \donttest{
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)
# }