Likelihood analysis of joint marginal and conditional models for longitudinal categorical data.

The authors develop a Markov model for the analysis of longitudinal categorical data which facilitates modelling both marginal and conditional structures. A likelihood formulation is employed for inference, so the resulting estimators enjoy the optimal properties such as efficiency and consistency, and remain consistent when data are missing at random. Simulation studies demonstrate that the proposed method performs well under a variety of situations. Application to data from a smoking prevention study illustrates the utility of the model and interpretation of covariate effects. The Canadian Journal of Statistics © 2009 Statistical Society of Canada Les auteurs developpent un modele de Markov pour l'analyse de donnees categorielles longitudinales facilitant la representation des structures marginales et conditionnelles. L'inference est basee sur une fonction de vraisemblance afin d'obtenir des estimateurs efficaces, coherents et qui le demeurent lorsqu'il y a des donnees manquantes au hasard. Des etudes de simulation montrent que la methode proposee se comporte bien dans les differents scenarios consideres. L'application a des donnees provenant d'une etude sur la lutte contre le tabagisme illustre bien l'utilite de ce modele et permet une interpretation des effets des covariables. La revue canadienne de statistique © 2009 Societe statistique du Canada

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