COMMENGES, Daniel; GÉGOUT-PETIT, Anne; JOLY, Pierre; LIQUET, Benoit

doi:10.1111/j.1467-9469.2006.00536.x

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COMMENGES, Daniel
Biostatistique
Epidémiologie, santé publique et développement

GÉGOUT-PETIT, Anne
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]

JOLY, Pierre
Biostatistique

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Ce document a été publié dans

Scandinavian Journal of Statistics. 2007-03, vol. 34, n° 1, p. 33-52

Wiley

Résumé en anglais

We consider models based on multivariate counting processes, including multi‐state models. These models are specified semi‐parametrically by a set of functions and real parameters. We consider inference for these models based on coarsened observations, focusing on families of smooth estimators such as produced by penalized likelihood. An important issue is the choice of model structure, for instance, the choice between a Markov and some non‐Markov models. We define in a general context the expected Kullback–Leibler criterion and we show that the likelihood‐based cross-validation (LCV) is a nearly unbiased estimator of it. We give a general form of an approximate of the leave‐one‐out LCV. The approach is studied by simulations, and it is illustrated by estimating a Markov and two semi‐Markov illness–death models with application on dementia using data of a large cohort study.< Réduire

Mots clés en anglais

counting processes

cross-validation

dementia

interval-censoring

Kullback–Leibler loss

Markov models

multi-state models

penalized likelihood

semi-Markov models

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Choice between Semi-parametric Estimators of Markov and Non-Markov Multi-state Models from Coarsened Observations

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