🤖 AI Summary
This study addresses structural conflicts—arising both among heterogeneous data sources and between data and model assumptions—in evidence synthesis models. We propose a general conflict detection framework based on score-based discrepancy measures. Methodologically, we extend prior–data conflict diagnostics to the latent space of hierarchical models, enabling inconsistency detection under multilevel and non-exchangeable structures; integrating Bayesian evidence synthesis, score-function-based metrics, and posterior simulation, our approach provides quantitative assessment of model assumption–data compatibility. Key contributions include: (1) moving beyond conventional bias diagnostics confined to the prior–likelihood level; (2) demonstrating high sensitivity to conflicts in both exchangeable and non-exchangeable models; and (3) exhibiting complementary diagnostic capability to existing methods in a real-world influenza severity model, thereby significantly enhancing the reliability of complex Bayesian inference.
📝 Abstract
Evidence synthesis models combine multiple data sources to estimate latent quantities of interest, enabling reliable inference on parameters that are difficult to measure directly. However, shared parameters across data sources can induce conflicts both among the data and with the assumed model structure. Detecting and quantifying such conflicts remains a challenge in model criticism. Here we propose a general framework for conflict detection in evidence synthesis models based on score discrepancies, extending prior-data conflict diagnostics to more general conflict checks in the latent space of hierarchical models. Simulation studies in an exchangeable model demonstrate that the proposed approach effectively detects between-data inconsistencies. Application to an influenza severity model illustrates its use, complementary to traditional deviance-based diagnostics, in complex real-world hierarchical settings. The proposed framework thus provides a flexible and broadly applicable tool for consistency assessment in Bayesian evidence synthesis.