Functional clustering methods for binary longitudinal data with temporal heterogeneity

• Published in: Computational statistics & data analysis Vol. 185; p. 107766
• Main Authors: Sohn, Jinwon, Jeong, Seonghyun, Cho, Young Min, Park, Taeyoung
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2023
• Subjects: data analysis; Dirichlet process; Longitudinal data; Partial collapsed Gibbs sampler; Probit mixed models; statistics; Varying-coefficients; Dirichlet process; Longitudinal data; Partial collapsed Gibbs sampler; Varying-coefficients; Probit mixed models
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2023.107766

In the analysis of binary longitudinal data, it is of interest to model a dynamic relationship between a response and covariates as a function of time, while also investigating similar patterns of time-dependent interactions. We present a novel generalized varying-coefficient model that accounts for within-subject variability and simultaneously clusters varying-coefficient functions, without restricting the number of clusters nor overfitting the data. In the analysis of a heterogeneous series of binary data, the model extracts population-level fixed effects, cluster-level varying effects, and subject-level random effects. Various simulation studies show the validity and utility of the proposed method to correctly specify cluster-specific varying-coefficients when the number of clusters is unknown. The proposed method is applied to a heterogeneous series of binary data in the German Socioeconomic Panel (GSOEP) study, where we identify three major clusters demonstrating the different varying effects of socioeconomic predictors as a function of age on the working status.