Journal Article

Who benefits? Uncovering hidden heterogeneity of treatment effects in adaptive trials using Bayesian methods: a systematic review

Giblon, Rachel, Liu, Kuan, Goligher, Ewan C., Cunningham, Jessie, Ouyang, Yongdong, Gao, Chengyang, Heath, Anna, Pimienta, Allen L.

2025

Overview

Background Adaptive clinical trials increasingly aim to detect heterogeneity of treatment effect (HTE) to guide personalized care. However, 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 consideration of 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, reproducibility, and whether equity-related factors were explicitly considered. 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 2826 screened records, seven studies met inclusion criteria. Bayesian methods included random partition models, spatial models, logistic regression with dimension reduction, adaptive randomization using machine learning classifiers, and adaptive enrichment or platform designs incorporating model averaging or latent subgroup estimation. In simulation studies, these methods often showed improvements in subgroup detection, efficiency, or power relative to non-Bayesian comparators. None were tested using real-world trial data. Reproducibility was limited overall, with analytic code only available for the three most recent studies. Notably, none explicitly framed their methods as tools to address inequities in treatment outcomes across population subgroups. Conclusions The small number of simulation-based studies illustrates preliminary but promising directions for applying Bayesian methods to detect HTE in adaptive clinical trials. While these approaches demonstrate potential to enhance trial adaptability, scalability, and inclusiveness, current evidence remains limited and largely conceptual. Incorporating an equity lens into future methodological development, alongside greater emphasis on empirical validation and open science practices, will be essential to determine their practical value in advancing equitable clinical research.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Adaptation / Adaptive clinical trials / Adaptive Clinical Trials as Topic - methods / Adaptive Clinical Trials as Topic - statistics & numerical data / Bayes Theorem / Bayesian analysis / Bayesian statistics / Biomarkers / Biomedicine / Clinical trials / Decision making / diversity / Equity / Evidence-based medicine / Health aspects / Health Sciences / Heterogeneity of treatment effect (HTE) / Humans / Improving equity / Inclusion / inclusion in randomised trials / Machine learning / Medical research / Medical treatment / Medicine / Medicine & Public Health / Medicine, Experimental / Patients / Personalized medicine / Random variables / Randomized Controlled Trials as Topic - methods / Reproducibility of Results / Research Design - statistics & numerical data / Review / Simulation methods / Statistical methods / Statistics for Life Sciences / Subgroup analysis / Systematic review / Treatment Effect Heterogeneity / Treatment Outcome