Predictions for an illnessdeath 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
1 2 3 
Arguments
object 
an 
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 ( 
... 
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 
F01 
the lifetime risk of
disease. 
F0. 
the probability of
exit from state 0. 
Author(s)
R: Celia Touraine <Celia.Touraine@isped.ubordeaux2.fr> Fortran: Pierre Joly <Pierre.Joly@isped.ubordeaux2.fr>
See Also
idm
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  ## 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)
