lcmm (version 1.5.7)

Diffepoce: Computation of the difference of EPOCE estimators and its 95

Description

This functions computes the difference of CVPOL or MPOL and its 95

Usage

Diffepoce(epoceM1,epoceM2)

Arguments

epoceM1

a first object inheriting from class epoce

epoceM2

a second object inheriting from class epoce

Value

• call.Jointlcmm1
  the Jointlcmm call for the first epoce object in epoceM1
• call.Jointlcmm2
  the Jointlcmm call for the second epoce object in epoceM2
• call
  the matched call
• DiffEPOCE
  Dataframe containing, for each prediction time s, the difference in either MPOL or CVPOL depending on the dataset used, and the 95
• new.data
  a boolean for internal use only, which is FALSE if computation is done on the same data as for Jointlcmm estimation, and TRUE otherwise.

Details

From the EPOCE estimates and the individual contributions to the prognostic observed log-likelihood obtained on the same dataset with epoce for two different joint latent class models, the difference of CVPOL (or MPOL) and its 95 Delta(MPOL) +/- qnorm(0.95)*sqrt(VARIANCE) for an external dataset Delta(CVPOL) +/- qnorm(0.95)*sqrt(VARIANCE) for the dataset used in Jointlcmm

where Delta(CVPOL) (Delta(MPOL)) is the difference of CVPOL or MPOL of the two joint latent class models, and VARIANCE is the empirical variance of the difference of individual contributions to the prognostic observed log-likelihoods of the two joint latent class models.

See Commenges et al. (2012) for further details.

References

Commenges, Liquet and Proust-Lima (2012). Choice of prognostic estimators in joint models by estimating differences of expected conditional {K}ullback-{L}eibler risks. Biometrics - in press

Proust-Lima, Sene, Taylor and Jacqmin-Gadda (2012). Joint latent class models of longitudinal and time-to-event data: a review. Statistical Methods in Medical Research - in press

See Also

Jointlcmm,epoce,summary.Diffepoce

Examples

#### estimation with 2 latent classes (ng=2)
data(data_Jointlcmm)
m2 <- 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_Jointlcmm,
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))
m1 <- Jointlcmm(fixed= Ydep1~Time*X1,random=~Time,subject='ID'
,survival = Surv(Tevent,Event)~ X1+X2 ,hazard="Weibull"
,hazardtype="PH",ng=1,data=data_Jointlcmm,
B=c(-7.6634,  0.9136,  0.1002,  0.6641, 10.5675, -1.6589,  1.4767, -0.0806,
  0.9240,0.5643,  1.2277,  1.5004))

## EPOCE computation for predictions times from 1 to 6 on the dataset used for estimation of m.
VecTime <- c(1,3,5,7,9,11,13,15)
cvpol1 <- epoce(m1,var.time="Time",pred.times=VecTime)
cvpol1
cvpol2 <- epoce(m2,var.time="Time",pred.times=VecTime)
cvpol2
DeltaEPOCE <- Diffepoce(cvpol1,cvpol2)
summary(DeltaEPOCE)
plot(DeltaEPOCE,bty="l")