predict.idm: Predictions for an illness-death model using either a...

In tagteam/SmoothHazard: Estimation of Smooth Hazard Models for Interval-Censored Data with Applications to Survival and Illness-Death Models

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 class 'idm'
predict(object, s, t, newdata, nsim = 200, seed = 21,
  conf.int = 0.95, lifeExpect = FALSE, maxtime, ...)

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.

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.
RM	the restricted mean survival time without disease.

or a list containing the following life expectancies with pointwise confidence intervals (when lifeExpect=TRUE):

LE.0.	Total life expectancy
LE.00	Life expectancy free of the disease (ie. Healthy life expectancy)
LE.01	Life expectancy with the disease (for a non-disease subject at time=s) (Note that LE.01=LE.0.-LE.00).
LE.11	Life expectancy for diseased subject at time=s
LTR	Life-time risk of the disease.

Author(s)

R: Celia Touraine <[e-mail]> and Thomas Alexander Gerds <[e-mail]> Fortran: Pierre Joly <[e-mail]>

See Also

idm

Examples

## Not run: 
set.seed(100)
d=simulateIDM(n = 100)
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)



## End(Not run)

tagteam/SmoothHazard documentation built on April 5, 2024, 6:32 a.m.