Who Benefits? Uncovering Hidden Heterogeneity of Treatment Effects in Adaptive Trials Using Bayesian Methods: A Systematic Review

Background. Adaptive clinical trials increasingly aim to detect heterogeneity of treatment effect (HTE) to guide personalized care. Yet most adaptive designs rely on predefined subgroups and are limited in their ability to uncover unknown or complex sources of HTE. Bayesian statistical methods offer a flexible alternative, enabling real-time learning and adaptation within trials. This review evaluates Bayesian methods used to detect hidden HTE in adaptive clinical trials, with attention to their methodological innovations, operating characteristics, and implications for equity and inclusion in trial design. Methods. We conducted a systematic search of MEDLINE, Embase, and other databases to identify original studies that developed Bayesian methods for detecting unknown HTE within adaptive clinical trial designs. Eligible studies were reviewed and synthesized based on design features, statistical methodology, operating characteristics, equity considerations, and reproducibility. Equity considerations included whether studies incorporated variables related to underrepresented populations—such as age, sex, race/ethnicity, or geography—examined intersectional subgroup effects, or explicitly framed their methods as tools to address health disparities. Results. Of 1,728 screened records, five studies met inclusion criteria. Bayesian methods included random partition models, spatial models, Bayesian logistic regression with dimension reduction, and Bayesian adaptive randomization using machine learning classifiers. Simulations showed these methods improved subgroup detection, efficiency, and power compared to non-Bayesian comparators. However, real-world validation was absent, and reproducibility was limited with only one study publicly sharing code. Critically, no studies explicitly examined how Bayesian HTE detection could be applied to address inequities in treatment outcomes across intersectional population subgroups. Conclusions. Bayesian methods offer promising avenues for improving the efficiency and precision of adaptive trials by enabling real-time HTE detection, with untapped potential to promote equity and inclusion in clinical research. Implementation barriers remain, including reproducibility, scalability to high-dimensional data, and reliance on simulations. Incorporating an equity lens into the design and reporting of Bayesian HTE methods could help trials better account for underserved populations and intersectional diversity. More robust evaluation frameworks and open science practices are needed to support broader, equitable application in clinical trial design.