Choice between semi-parametric estimators of Markov and non-Markov multi-state models from coarsened observations

Daniel Commenges*, Pierre Joly, Anne Gégout-Petit, Benoit Liquet

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

55 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)33-52
Number of pages20
JournalScandinavian Journal of Statistics
Volume34
Issue number1
DOIs
Publication statusPublished - Mar 2007
Externally publishedYes

Keywords

  • counting processes
  • cross-validation
  • dementia
  • interval-censoring
  • Kullback– Leibler loss
  • Markov models
  • multi-state models
  • penalized likelihood
  • semi-Markov models

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