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A BAYESIAN GROWTH MIXTURE MODEL FOR COMPLEX SURVEY DATA: CLUSTERING POSTDISASTER PTSD TRAJECTORIES.

Rebecca Anthopolos1, Qixuan Chen2, Joseph Sedransk3

  • 1Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine.

The Annals of Applied Statistics
|March 9, 2026
PubMed
Summary

This study introduces a Bayesian growth mixture model (GMM) for complex survey data, offering reduced bias and increased efficiency compared to traditional methods. The approach effectively identifies post-traumatic stress disorder (PTSD) trajectories after Hurricane Ike.

Keywords:
Complex survey sampleGibbs samplinggrowth mixture modelpost-traumatic stress disorderspatial modeling

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Area of Science:

  • Statistics
  • Biostatistics
  • Survey Methodology

Background:

  • Growth mixture models (GMMs) are underutilized for complex survey data.
  • Existing pseudo-likelihood methods may lead to efficiency loss due to survey weighting.
  • Complex sample designs (stratification, clustering) require specialized analytical approaches.

Purpose of the Study:

  • To propose a Bayesian GMM that incorporates complex sample design features.
  • To improve estimation bias and efficiency for survey data analysis.
  • To analyze longitudinal post-traumatic stress disorder (PTSD) trajectories in a hurricane-affected population.

Main Methods:

  • Developed a Bayesian GMM incorporating sample design features as covariates or variance components.
  • Implemented an efficient Gibbs sampler with closed-form conditional distributions.
  • Applied the model to data from the Galveston Bay Recovery Study (GBRS) with a stratified multi-stage cluster design.

Main Results:

  • Identified four clinically meaningful PTSD trajectory subgroups among residents of southeastern Texas post-Hurricane Ike.
  • Characterized risk factors associated with different PTSD trajectory subgroup memberships.
  • Demonstrated potential for reduced bias and increased efficiency compared to pseudo-likelihood methods.

Conclusions:

  • The proposed Bayesian GMM provides a robust framework for analyzing complex survey data.
  • The method offers advantages in bias reduction and efficiency, particularly when design features are informative.
  • An accompanying R package, Bsvygmm, is available for practical implementation.