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Titre du document / Document title

Hidden markov models for longitudinal comparisons

Auteur(s) / Author(s)

SCOTT Steven L. (1) ; JAMES Gareth M. (1) ; SUGAR Catherine A. (1) ;

Affiliation(s) du ou des auteurs / Author(s) Affiliation(s)

(1) The Marshall School of Business, University of Southern California, Los Angeles, CA 90089, ETATS-UNIS

Résumé / Abstract

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 in comparison with the current methodology.

Revue / Journal Title

Journal of the American Statistical Association    ISSN  0162-1459   CODEN JSTNAL 

Source / Source

2005, vol. 100, no470, pp. 359-369 [11 page(s) (article)] (30 ref.)

Langue / Language

Anglais

Editeur / Publisher

American Statistical Association, Alexandria, VA, ETATS-UNIS  (1922) (Revue)

Mots-clés anglais / English Keywords

Health state

;

Shrinkage estimator

;

Hierarchical model

;

Methodology

;

Homogeneity

;

Uncertainty

;

Transition state

;

Statistical method

;

Schizophrenia

;

Probability distribution

;

Transition probability

;

Conditional distribution

;

Bayes estimation

;

Parameter estimation

;

Cluster analysis (statistics)

;

State space method

;

Homogeneous model

;

Multivariate analysis

;

MCMC algorithm

;

Hidden Markov models

;

Hidden Markov model

;

Mots-clés français / French Keywords

Etat sante

;

Estimateur rétrécissement

;

Modèle hiérarchique

;

Clustering

;

Donnée longitudinale

;

Méthodologie

;

Homogénéité

;

Incertitude

;

Etat transition

;

Méthode statistique

;

Schizophrénie

;

Loi probabilité

;

Probabilité transition

;

Loi conditionnelle

;

Estimation Bayes

;

Estimation paramètre

;

Classification automatique (statistiques)

;

Méthode espace état

;

Modèle homogène

;

Analyse multivariable

;

Algorithme MCMC

;

Modèle Markov variable cachée

;

Modèle Markov caché

;

Mots-clés espagnols / Spanish Keywords

Metodología

;

Homogeneidad

;

Incertidumbre

;

Estado transitorio

;

Método estadístico

;

Esquizofrenia

;

Ley probabilidad

;

Probabilidad transición

;

Ley condicional

;

Estimación Bayes

;

Estimación parámetro

;

Método espacio estado

;

Modelo homogéneo

;

Análisis multivariable

;

Algoritmo MCMC

;

Modelo Markov oculto

;

Mots-clés d'auteur / Author Keywords

Health state model

;

Hierarchical model

;

Inhomogeneous hidden Markov model

;

k-means clustering

;

Markov chain Monte Carlo

;

Localisation / Location

INIST-CNRS, Cote INIST : 3094, 35400012475910.0010

Nº notice refdoc (ud4) : 16792621



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