DeepAI AI Chat
Log In Sign Up

The Autoregressive Structural Model for analyzing longitudinal health data of an aging population in China

by   Yazhuo Deng, et al.
University of Idaho

We seek to elucidate the impact of social activity, physical activity and functional health status (factors) on depressive symptoms (outcome) in the China Health and Retirement Longitudinal Study (CHARLS), a multi-year study of aging involving 20,000 participants 45 years of age and older. Although a variety of statistical methods are available for analyzing longitudinal data, modeling the dynamics within a complex system remains a difficult methodological challenge. We develop an Autoregressive Structural Model (ASM) to examine these factors on depressive symptoms while accounting for temporal dependence. The ASM builds on the structural equation model and also consists of two components: a measurement model that connects observations to latent factors, and a structural model that delineates the mechanism among latent factors. Our ASM further incorporates autoregressive dependence into both components for repeated measurements. The results from applying the ASM to the CHARLS data indicate that social and physical activity independently and consistently mitigated depressive symptoms over the course of five years, by mediating through functional health status.


A Riemann Manifold Model Framework for Longitudinal Changes in Physical Activity Patterns

Physical activity (PA) is significantly associated with many health outc...

Modeling Heterogeneity and Missing Data of Multiple Longitudinal Outcomes in Electronic Health Records

In electronic health records (EHRs), latent subgroups of patients may ex...

A Bayesian Approach to Modelling Longitudinal Data in Electronic Health Records

Analyzing electronic health records (EHR) poses significant challenges b...

Person as Population: A Longitudinal View of Single-Subject Causal Inference for Analyzing Self-Tracked Health Data

Single-subject health data are becoming increasingly available thanks to...

A Latent Gaussian Process Model for Analyzing Intensive Longitudinal Data

Intensive longitudinal studies are becoming progressively more prevalent...

Activity Self-Tracking with Smart Phones: How to Approach Odd Measurements?

Tracking physical activity reliably is becoming central to many research...

Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data

Panel data involving longitudinal measurements of the same set of partic...

Code Repositories


This repo contains the mplus code for the paper "The Autoregressive Structural Model for analyzing longitudinal health data of an aging population in China".

view repo