predict.idm: Predictions for an illness-death model using either a penalized likelihood approach or a Weibull parametrization.

Description

Predict transition probabilities and cumulative probabilities from an object of class idmSplines with confidence intervals are calculated.

Usage

# S3 method for idm
predict(
  object,
  s,
  t,
  newdata,
  nsim = 200,
  seed = 21,
  conf.int = 0.95,
  lifeExpect = FALSE,
  maxtime,
  ...
)

Value

a list containing the following predictions with pointwise confidence intervals:

p00: the transition probability \(p_{00}\).
p01: the transition probability \(p_{01}\).
p11: the transition probability \(p_{11}\).
p12: the transition probability \(p_{12}\).
p02_0: the probability of direct transition from state 0 to state 2.
p02_1: the probability of transition from state 0 to state 2 via state 1.
p02: transition probability \(p_{02}\). Note that p02=p_02_0+p02_1.
F01: the lifetime risk of disease. F01=p01+p02_1.
F0.: the probability of exit from state 0. F0.=p02_0+p01+p02_1.

Arguments

object: an idm class objects returned by a call to the idm function
s: time point at which prediction is made.
t: time horizon for prediction.
newdata: A data frame with covariate values for prediction.
nsim: number of simulations for the confidence intervals calculations. The default is 200.
seed: Seed passed to set.seed for Monte Carlo simulation of confidence intervals.
conf.int: Level of confidence, i.e., a value between 0 and 1, the default is 0.95. The default is also used when conf.int=TRUE. To avoid computation of confidence intervals, set conf.int to FALSE or NULL.
lifeExpect: Logical. If TRUE compute life expectancies, i.e., t=Inf.
maxtime: The upper limit of integration for calculations of life expectancies from Weibull parametrizations.
...: other parameters.

Author

R: Celia Touraine <Celia.Touraine@isped.u-bordeaux2.fr> and Thomas Alexander Gerds <tag@biostat.ku.dk> Fortran: Pierre Joly <Pierre.Joly@isped.u-bordeaux2.fr>

Examples

Run this code


# \donttest{
set.seed(100)
d=simulateIDM(n = 200)
fit <- 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)
predict(fit,s=0,t=80,conf.int=FALSE,lifeExpect=FALSE)
predict(fit,s=0,t=80,nsim=4,conf.int=TRUE,lifeExpect=FALSE)
predict(fit,s=0,t=80,nsim=4,conf.int=FALSE,lifeExpect=TRUE)

data(Paq1000)
library(prodlim)
fit.paq <- idm(formula02=Hist(time=t,event=death,entry=e)~certif,
formula01=Hist(time=list(l,r),event=dementia)~certif,data=Paq1000)

predict(fit.paq,s=70,t=80,newdata=data.frame(certif=1))
predict(fit.paq,s=70,lifeExpect=TRUE,newdata=data.frame(certif=1))

fit.splines <-  idm(formula02=Hist(time=t,event=death,entry=e)~certif,
		formula01=Hist(time=list(l,r),event=dementia)~certif,
                formula12=~1,
                method="Splines",
		data=Paq1000)

predict(fit.splines,s=70,t=80,newdata=data.frame(certif=1))
predict(fit.splines,s=70,t=80,lifeExpect=TRUE,newdata=data.frame(certif=1),nsim=20)


# }