Marginalized transition random effect models for multivariate longitudinal binary data

Date

2007-03-01

Author

İlk Dağ, Özlem

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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.

Subject Keywords

Statistics, Probability and Uncertainty, Statistics and Probability

URI

https://hdl.handle.net/11511/37404

Journal

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE

DOI

https://doi.org/10.1002/cjs.5550350110

Collections

Department of Statistics, Article

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Citation Formats

Ö. İlk Dağ, “Marginalized transition random effect models for multivariate longitudinal binary data,” CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, pp. 105–123, 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37404.