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Marginalized transition random effect models for multivariate longitudinal binary data
Author(s) -
Ilk Ozlem,
Daniels Michael J.
Publication year - 2007
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.5550350110
Subject(s) - multivariate statistics , marginal model , random effects model , covariate , statistics , binary data , markov chain monte carlo , logistic regression , mathematics , econometrics , markov chain , generalized linear mixed model , binary number , regression analysis , computer science , monte carlo method , arithmetic , medicine , meta analysis
Generalized linear models with random effects and/or serial dependence are commonly used to analyze longitudinal data. However, the computation and interpretation of marginal covariate effects can be difficult. This led Heagerty (1999, 2002) to propose models for longitudinal binary data in which a logistic regression is first used to explain the average marginal response. The model is then completed by introducing a conditional regression that allows for the longitudinal, within‐subject, dependence, either via random effects or regressing on previous responses. In this paper, the authors extend the work of Heagerty to handle multivariate longitudinal binary response data using a triple of regression models that directly model the marginal mean response while taking into account dependence across time and across responses. Markov Chain Monte Carlo methods are used for inference. Data from the Iowa Youth and Families Project are used to illustrate the methods.