Description Usage Arguments Details Value Author(s) References See Also Examples
This function computes estimators of the Expected Prognostic Observed
CrossEntropy (EPOCE) for evaluating the predictive accuracy of joint latent
class models estimated using Jointlcmm
. On the same data as used for
estimation of the Jointlcmm
object, this function computes both the
Mean Prognostic Observed LogLikelihood (MPOL) and the CrossValidated
Observed LogLikelihood (CVPOL), two estimators of EPOCE. The latter
corrects the MPOL estimate for overoptimism by approximated
crossvalidation. On external data, this function only computes the Mean
Prognostic Observed LogLikelihood (MPOL).
1 2 
model 
an object inheriting from class 
pred.times 
Vector of times of prediction, from which predictive accuracy is evaluated (only subjects still at risk at the time of prediction are included in the computation, and only information before the time of prediction is considered. 
var.time 
Name of the variable indicating time in the dataset 
fun.time 
an optional function. This is only required if the time
scales in the longitudinal part of the model and the survival part are
different. In that case, 
newdata 
optional. When missing, the data used for estimating the

subset 
a specification of the rows to be used: defaults to all rows. This can be any valid indexing vector for the rows of data or if that is not supplied, a data frame made up of the variable used in formula. 
na.action 
Integer indicating how NAs are managed. The default is 1 for 'na.omit'. The alternative is 2 for 'na.fail'. Other options such as 'na.pass' or 'na.exclude' are not implemented in the current version. 
This function does not apply for the moment with multiple causes of event (competing risks).
EPOCE assesses the prognostic information of a joint latent class model. It relies on information theory.
MPOL computed at time s equals minus the mean individual contribution to the conditional loglikelihood of the time to event given the longitudinal data up to the time of prediction s and given the subject is still at risk of event in s.
CVPOL computed at time s equals MPOL at time s plus a penalty term that corrects for overoptimism when computing predictive accuracy measures on the same dataset as used for estimation. This penalty term is computed from the inverse of the Hessian of the joint loglikelihood and the product of the gradients of the contributions to respectively the joint loglikelihood and the conditional loglikelihood.
The theory of EPOCE and its estimators MPOL and CVPOL is given in Commenges et al. (2012), and further detailed and illustrated for joint models in ProustLima et al. (2013).
call.Jointlcmm 
the 
call.epoce 
the matched call 
EPOCE 
Dataframe containing, for
each prediction time s, the number of subjects still at risk at s (and with
at least one measure before s), the number of events after time s, the MPOL,
and the CVPOL when computation is done on the dataset used for

IndivContrib 
Individual contributions to the prognostic observed loglikelihood at each time of prediction. Used for computing tracking intervals of EPOCE differences between models. 
new.data 
a boolean for internal use only, which is FALSE if
computation is done on the same data as for 
Cecile ProustLima and Amadou Diakite
Commenges, Liquet and ProustLima (2012). Choice of prognostic estimators in joint models by estimating differences of expected conditional KullbackLeibler risks. Biometrics 68(2), 3807.
ProustLima, Sene, Taylor and JacqminGadda (2014). Joint latent class models of longitudinal and timetoevent data: a review. Statistical Methods in Medical Research 23, 7490.
Jointlcmm
, print.epoce
, summary.epoce
, plot.epoce
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  ## Not run:
## estimation of a joint latent class model with 2 latent classes (ng=2)
# (see the example section of Jointlcmm for details about
# the model specification)
m < Jointlcmm(fixed= Ydep1~Time*X1,random=~Time,mixture=~Time,subject='ID'
,survival = Surv(Tevent,Event)~ X1+X2 ,hazard="Weibull"
,hazardtype="PH",ng=2,data=data_lcmm,logscale=TRUE,
B=c(0.7608, 9.4974 , 1.0242, 1.4331 , 0.1063 , 0.6714, 10.4679, 11.3178,
2.5671, 0.5386, 1.4616, 0.0605, 0.9489, 0.1020 , 0.2079, 1.5045))
summary(m)
## Computation of the EPOCE on the same dataset as used for
# estimation of m with times at predictions from 1 to 15
VecTime < c(1,3,5,7,9,11,13,15)
cvpl < epoce(m,var.time="Time",pred.times=VecTime)
summary(cvpl)
plot(cvpl,bty="l",ylim=c(0,2))
## End(Not run)

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