Bayesian Joint Modeling of Longitudinal Symptomatology Scale Responses and Fall Outcomes via Heterogeneous Latent Transition Analysis

2026-08-01

AI Summary

This study addresses the identification of dynamic, evolving patterns in longitudinal, multidimensional women’s health symptoms that are associated with subsequent fall risk. To this end, the authors propose a heterogeneous latent transition analysis framework: individuals are first stratified into latent classes based on symptom response profiles, and then multilevel clustering is applied to sequences of class transitions, jointly modeling their association with fall outcomes. The method integrates Bayesian inference, latent transition modeling, and longitudinal categorical data analysis, and demonstrates strong performance in parameter estimation and cluster recovery, as validated through Monte Carlo simulations. Empirical analysis successfully uncovers several symptom trajectories that significantly predict fall risk, offering an effective tool for pattern discovery in complex longitudinal health data.

Abstract

The Study of Women's Health Across the Nation (SWAN) has followed women for over 30 years, from midlife premenopause until later life. The study has 16 surveys at approximately 2 years intervals that cover a wide range of physical and psychological symptoms. These multivariate categorical survey responses potentially contain rich health-related information. Temporal trajectories of the survey responses can be characterized by both the responses profiles and the evolving dynamics of the responses over time. To capture those two features and investigate how they inform subsequent health outcomes, we propose a joint multi-layer latent transition model. We combine a latent transition model that classifies individuals based on their response profiles over time with an additional layer of clustering of these latent class transition sequences, with the goal of connecting these cluster profiles with health outcomes: in this application, self-reported falls. In addition, we evaluate the operating characteristics of the method through simulation studies.

Problem

• longitudinal symptomatology
• latent transition analysis
• health outcomes
• fall risk
• multivariate categorical data

Innovation

• Bayesian joint modeling
• latent transition analysis
• heterogeneous trajectories
• longitudinal symptomatology
• fall outcomes