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Marginalized Models for Moderate to Long Series of Longitudinal Binary Response Data
Author(s) -
Schildcrout Jonathan S.,
Heagerty Patrick J.
Publication year - 2007
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2006.00680.x
Subject(s) - random effects model , binary data , statistics , econometrics , inference , bayesian probability , mathematics , latent variable , autocorrelation , series (stratigraphy) , missing data , marginal model , quasi likelihood , bayesian inference , binary number , computer science , regression analysis , count data , artificial intelligence , medicine , paleontology , meta analysis , arithmetic , biology , poisson distribution
Summary Marginalized models (Heagerty, 1999, Biometrics 55, 688–698) permit likelihood‐based inference when interest lies in marginal regression models for longitudinal binary response data. Two such models are the marginalized transition and marginalized latent variable models. The former captures within‐subject serial dependence among repeated measurements with transition model terms while the latter assumes exchangeable or nondiminishing response dependence using random intercepts. In this article, we extend the class of marginalized models by proposing a single unifying model that describes both serial and long‐range dependence. This model will be particularly useful in longitudinal analyses with a moderate to large number of repeated measurements per subject, where both serial and exchangeable forms of response correlation can be identified. We describe maximum likelihood and Bayesian approaches toward parameter estimation and inference, and we study the large sample operating characteristics under two types of dependence model misspecification. Data from the Madras Longitudinal Schizophrenia Study (Thara et al., 1994, Acta Psychiatrica Scandinavica 90, 329–336) are analyzed.