Détail de la notice
Titre du Document
Hidden markov models for longitudinal comparisons
Auteur(s)
SCOTT Steven L. ; JAMES Gareth M. ; SUGAR Catherine A.
Résumé
Medical researchers interested in temporal, multivariate measurements of complex diseases have recently begun developing health state models, which divide the space of patient characteristics into medically distinct clusters. The current state of the art in health services research uses k-means clustering to form the health states and a first-order Markov chain to describe transitions between the states. This fitting procedure ignores information from temporally adjacent observations and prevents uncertainty from parameter estimation and cluster assignments from being incorporated into the analysis. A natural way to address these issues is to combine clustering and longitudinal analyses using a hidden Markov model. We fit hidden Markov models to longitudinal data using Bayesian methods that account for all of the uncertainty in the parameters, conditional only on the underlying correctness of the model. Potential lack of time homogeneity in the Markov chain is accounted for by embedding transition probabilities into a hierarchical model that provides Bayesian shrinkage across time. We illustrate this approach by developing a hidden Markov health state model for comparing the effectiveness of clozapine and haloperidol, two antipsychotic medications for schizophrenia. We find that clozapine outperforms haloperidol and identify the types of patients in whom clozapine's advantage is greatest and weakest. Finally, we discuss the advantages and disadvantages of hidden Markov models
Editeur
American Statistical Association
Identifiant
ISSN : 0162-1459 CODEN : JSTNAL
Source
Journal of the American Statistical Association A. 2005, vol. 100, n° 470, pp. 359-369 [11 pages] [bibl. : 30 ref.]
Langue
Anglais
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