A Bayesian growth mixture model for complex survey data: Clustering postdisaster PTSD trajectories
Bibliographic record
Abstract
Research on growth mixture models (GMMs) for analyzing data from a complex sample survey is sparse. Existing methods use pseudo-likelihood in which survey weights are incorporated into the likelihood function, with variance estimated via linearization or resampling techniques. Despite popularity of the pseudo-likelihood approach, weighted estimation introduces the risk of efficiency loss. In this paper we propose a Bayesian GMM for complex survey data in which sample design features, such as stratification, clustering, and unequal probability of selection, are incorporated as covariates or hierarchical variance components. The Bayesian GMM can yield a reduction in bias in the estimation of regression coefficients when design features are associated with survey outcomes, and can lead to more efficient estimates than the pseudo-likelihood estimators when the design is noninformative. We develop an efficient Gibbs sampler that includes only closed-form full conditional distributions for model fitting. We present the results of a careful analysis of data from the Galveston Bay Recovery Study (GBRS) which used a stratified multi-stage cluster sample design. Using our proposed Bayesian GMM, we characterize longitudinal trajectories of post-traumatic stress disorder (PTSD) among residents of southeastern Texas in the aftermath of Hurricane Ike. We identify four clinically meaningful PTSD trajectory subgroups and characterize risk factors associated with subgroup membership. In the absence of existing software that can be used to implement our proposed Bayesian GMM for complex survey data, we built the R package Bsvygmm for model fitting, selection, and checking which can be downloaded from https://github.com/anthopolos/Bsvygmm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".