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Modeling local dependence in latent vector autoregressive models.

Trung Dung Tran¹, Emmanuel Lesaffre¹, Geert Verbeke¹

¹Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Katholieke Universiteit Leuven, Kapucijnenvoer 35, B-3000 Leuven, Belgium and Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Universiteit Hasselt, B-3590 Diepenbeek, Belgium.

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Summary

This summary is machine-generated.

This study introduces a Bayesian latent vector autoregressive (LVAR) model to improve analysis of longitudinal data. The model corrects biased estimates caused by ignoring local dependence in responses, enhancing accuracy for latent variable models.

Keywords:

Bayesian approach Local dependence Local independence Oral health and general health Vector autoregressive models

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Area of Science:

Statistics
Biostatistics
Longitudinal Data Analysis

Background:

Multivariate longitudinal data analysis often assumes local independence.
This assumption may not hold in practice, leading to biased parameter estimates.
Existing methods struggle to account for local dependence in latent variable models.

Purpose of the Study:

To propose a Bayesian latent vector autoregressive (LVAR) model for analyzing multivariate longitudinal data.
To address and correct for local dependence in responses, which is often ignored.
To accurately estimate model parameters, particularly regression coefficients in the LVAR process.

Main Methods:

Developed a Bayesian latent vector autoregressive (LVAR) model.
Incorporated item-specific random effects to account for local dependence.
Conducted simulation studies to evaluate model performance and bias correction.
Applied the model to real-world data from an elderly population registry.

Main Results:

Wrongly assuming local independence leads to biased estimates of LVAR regression coefficients and item parameters.
The proposed LVAR model successfully corrects these biased estimates.
The model quantifies the magnitude of local dependence, providing insights into data structure.

Conclusions:

The Bayesian LVAR model with item-specific random effects provides accurate parameter estimation for longitudinal data with local dependence.
Ignoring local dependence can significantly distort findings in latent variable modeling.
The model offers a robust approach for analyzing complex longitudinal datasets, as demonstrated in the elderly health registry example.