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

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## S3 method for class 'idm'
predict(object, s, t, Z01, Z02, Z12, nsim = 2000, CI = TRUE,
  ...)

Arguments

object

an idm class objects returned by a call to the idm function

s

time at prediction.

t

time for prediction.

Z01

vector for the values of the covariates on the transition 0 –> 1 (in the same order as the covariates within the call. The default values are all 0.

Z02

vector for the values of the covariates on the transition 0 –> 2 (in the same order as the covariates within the call. The default values are all 0.

Z12

vector for the values of the covariates on the transition 1 –> 2 (in the same order as the covariates within the call. The default values are all 0.

nsim

number of simulations for the confidence intervals calculations. The default is 2000.

CI

boolean: with (TRUE) or without (WRONG) confidence intervals for the predicted values. The default is TRUE.

...

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.

Author(s)

R: Celia Touraine <Celia.Touraine@isped.u-bordeaux2.fr> Fortran: Pierre Joly <Pierre.Joly@isped.u-bordeaux2.fr>

See Also

idm

Examples

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## Not run: 
data(Paq1000)
library(prodlim)
fit <- idm(formula02=Hist(time=t,event=death,entry=e)~certif,
formula01=Hist(time=list(l,r),event=dementia)~certif,data=Paq1000)

pred <- predict(fit,s=70,t=80,Z01=c(1),Z02=c(1),Z12=c(1))
pred

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)

pred <- predict(fit.splines,s=70,t=80,Z01=c(1),Z02=c(1))
pred


## End(Not run)