Analysis of binary longitudinal data with time-varying effects

This paper considers the analysis of longitudinal data where a binary response variable is observed repeatedly for each subject over time. In analyzing such data, regression coefficients are commonly assumed constant over time, which may not properly account for the time-varying effects of some subj...

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Bibliographic Details
Published in:Computational statistics & data analysis 2017-08, Vol.112, p.145-153
Main Authors: Jeong, Seonghyun, Park, Minjae, Park, Taeyoung
Format: Article
Language:English
Subjects:
Longitudinal data
Nonparametric regression
Partial collapse
Probit mixed model
Repeated measures
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Summary:This paper considers the analysis of longitudinal data where a binary response variable is observed repeatedly for each subject over time. In analyzing such data, regression coefficients are commonly assumed constant over time, which may not properly account for the time-varying effects of some subject characteristics on a sequence of binary outcomes. This paper proposes a Bayesian method for the analysis of binary longitudinal data with time-varying regression coefficients and random effects to account for nonlinear subject-specific effects over time as well as between-subject variation. The proposed method facilitates posterior computation via the method of partial collapse and accommodates spatially inhomogeneous smoothness of nonparametric functions without overfitting via a basis search technique. The proposed method is illustrated with a simulated study and the binary longitudinal data from the German socioeconomic panel study.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2017.03.007