Likelihood Formulae for generally coarsened observations from Multistate models
GÉGOUT-PETIT, Anne
Institut de Mathématiques de Bordeaux [IMB]
Epidémiologie, santé publique et développement
Institut de Mathématiques de Bordeaux [IMB]
Epidémiologie, santé publique et développement
GÉGOUT-PETIT, Anne
Institut de Mathématiques de Bordeaux [IMB]
Epidémiologie, santé publique et développement
< Reduce
Institut de Mathématiques de Bordeaux [IMB]
Epidémiologie, santé publique et développement
Language
en
Communication dans un congrès
This item was published in
Statistical Modelling and Inference in Life Sciences, 2005-09-29, Potsdam.
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 & G´egout-Petit. The approach is illustrated by considering models for dementia, institutionalization and deathRead less <
English Keywords
coarsening
counting processes
dementia
interval-censoring
likelihood
Markov models
multi state models
Origin
Hal imported