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arXiv · 2210.08385

A Joint Modeling Approach for Clustering Mixed-Type Multivariate Longitudinal Data: Application to the CHILD Cohort Study

Zhiwen Tan·Chang Shen·Padmaja Subbarao·Wendy Lou·Zihang Lu
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Abstract

In epidemiological and clinical studies, identifying patients' phenotypes based on longitudinal profiles is critical to understanding the disease's developmental patterns. The current study was motivated by data from a Canadian birth cohort study, the CHILD Cohort Study. Our goal was to use multiple longitudinal respiratory traits to cluster the participants into subgroups with similar longitudinal respiratory profiles in order to identify clinically relevant disease phenotypes. To appropriately account for distinct structures and types of these longitudinal markers, we proposed a novel joint model for clustering mixed-type (continuous, discrete and categorical) multivariate longitudinal data. We also developed a Markov Chain Monte Carlo algorithm to estimate the posterior distribution of model parameters. Analysis of the CHILD Cohort data and simulated data were presented and discussed. Our study demonstrated that the proposed model serves as a useful analytical tool for clustering multivariate mixed-type longitudinal data. We developed an R package BCClong to implement the proposed model efficiently.

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Zhiwen Tan, Chang Shen, Padmaja Subbarao, Wendy Lou, Zihang Lu. 2023-03-21. A Joint Modeling Approach for Clustering Mixed-Type Multivariate Longitudinal Data: Application to the CHILD Cohort Study. https://arxiv.org/abs/2210.08385

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