The performance of odds ratio estimation under different scenarios in Bayesian meta-analysis: A simulation study

This study presents a comprehensive evaluation of Bayesian meta-analysis methods for estimating odds ratios (ORs), with a focus on the impact of heterogeneity and prior distribution choices under varying conditions. Recognizing the limitations of frequentist approaches, especially in small-sample or rare-event scenarios, we implemented a Bayesian framework utilizing four different priors for heterogeneity: half-normal, exponential, half-Cauchy, and inverse-gamma. Simulation studies were conducted across 1,152 scenarios, varying the number of studies, event rarity, randomization ratios, and baseline risks. Results indicate that prior specification and study size substantially influence estimation accuracy, particularly for rare events. To further explore these interactions, CHAID (Chi-square Automatic Interaction Detection) analysis, which effectively identified key factors affecting model performance, is implemented. CHAID revealed that the number of studies included in the meta-analysis (NSMA) is the most significant determinant of estimation reliability, while other variables such as event type and randomization ratio exert notable influence under specific conditions. CHAID also facilitated the categorization of OR estimation quality and heterogeneity levels, offering a powerful visual and interpretive aid. Overall, this study underscores the importance of prior selection in Bayesian meta-analysis and highlights CHAID analysis as a valuable complementary tool for uncovering complex interactions and enhancing result interpretability.