Bayesian hierarchical models with calibrated mixtures of g-priors for assessing treatment effect moderation in meta-analysis
以校准的 g 先验混合层级模型估计荟萃分析中的处理效应调节
Assessing treatment effect moderation is critical in biomedical research and many other fields, as it guides personalized intervention strategies to improve participant's outcomes. Individual participant-level data meta-analysis (IPD-MA) offers a robust framework for such assessments by leveraging data from multiple trials. However, its performance is often compromised by challenges such as high between-trial variability. Traditional Bayesian shrinkage methods have gained…