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Bayesian Structural Equation Multi-State Model for Mediation Analysis
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DOI：10.1080/10705511.2026.2640014.png)
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Abstract
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This study introduces a joint modeling framework that integrates structural equation model (SEM) with multi-state semi-competing risks model to investigate the complex causal mechanism among exposure, latent mediators, and multiple hierarchically correlated survival outcomes. The framework first employs a linear SEM to characterize latent mediators underlying highly correlated observable surrogates. These latent mediators are then incorporated into semi-parametric proportional hazards models with frailty to assess their mediating roles in the relationships between exposure and hierarchically correlated survival outcomes. Estimation and inference are conducted within an integrated Bayesian framework leveraging Monte Carlo methods, Gibbs sampling, and the Metropolis-Hastings algorithm. Extensive simulation studies demonstrate the robustness and accuracy of the proposed method. Application to the Framingham Heart Study uncovers causal pathways linking smoking and metabolic health factors and evaluates their combined impact on long-term cardiovascular outcomes, offering new insights into the complex interplay between lifestyle factors and health risks.
Keywords:
Causal mediation analysis
latent variables
multi-state model
semi-competing risks
structural equation model
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