Likelihood for generally coarsened observations from multi-state or counting process models.
COMMENGES, Daniel
Biostatistique
Institut de Santé Publique, d'Epidémiologie et de Développement [ISPED]
Biostatistique
Institut de Santé Publique, d'Epidémiologie et de Développement [ISPED]
GÉGOUT-PETIT, Anne
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
COMMENGES, Daniel
Biostatistique
Institut de Santé Publique, d'Epidémiologie et de Développement [ISPED]
Biostatistique
Institut de Santé Publique, d'Epidémiologie et de Développement [ISPED]
GÉGOUT-PETIT, Anne
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
< Reduce
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Language
en
Article de revue
This item was published in
Scandinavian Journal of Statistics. 2007-06, vol. 34, n° 2, p. 432-450
Wiley
English Abstract
We consider first the mixed discrete-continuous scheme of observation in multistate models; this is a classical pattern in epidemiology because very often clinical status is assessed at discrete visit times while times of ...Read more >
We consider first the mixed discrete-continuous scheme of observation in multistate models; this is a classical pattern in epidemiology because very often clinical status is assessed at discrete visit times while times of death or other events are observed exactly. A heuristic likelihood can be written for such models, at least for Markov models; however, a formal proof is not easy and has not been given yet. We present a general class of possibly non-Markov multistate models which can be represented naturally as multivariate counting processes. We give a rigorous derivation of the likelihood based on applying Jacod's formula for the full likelihood and taking conditional expectation for the observed likelihood. A local description of the likelihood allows us to extend the result to a more general coarsening observation scheme proposed by Commenges & Gégout-Petit. The approach is illustrated by considering models for dementia, institutionalization and death.Read less <
English Keywords
coarsening
counting processes
dementia
interval-censoring
likelihood
Markov models
multistate models
Origin
Hal imported