Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions

2025-03-12

AI Summary

Traditional phase III randomized controlled trials (RCTs) face a tripartite challenge: ensuring regulatory compliance, enabling real-world deployability, and accurately estimating treatment effects for vulnerable subpopulations. To address this, we propose RFAN (Randomize First, Augment Next), a novel RCT paradigm that jointly models regulatory validation requirements and fairness-aware adaptive treatment allocation. RFAN integrates causal inference, deep Bayesian active learning, and a hybrid randomized–adaptive design. Evaluated on synthetic and semi-realistic datasets, RFAN maintains statistical power for primary endpoints while improving estimation accuracy for vulnerable groups by 32%. It thereby enhances dynamic adaptability to population heterogeneity and policy constraints. RFAN offers a methodologically rigorous, implementation-ready framework bridging regulatory science and health equity.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Machine Learning: Active Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Randomized Controlled Trials (RCTs) are the gold standard for evaluating the effect of new medical treatments. Treatments must pass stringent regulatory conditions in order to be approved for widespread use, yet even after the regulatory barriers are crossed, real-world challenges might arise: Who should get the treatment? What is its true clinical utility? Are there discrepancies in the treatment effectiveness across diverse and under-served populations? We introduce two new objectives for future clinical trials that integrate regulatory constraints and treatment policy value for both the entire population and under-served populations, thus answering some of the questions above in advance. Designed to meet these objectives, we formulate Randomize First Augment Next (RFAN), a new framework for designing Phase III clinical trials. Our framework consists of a standard randomized component followed by an adaptive one, jointly meant to efficiently and safely acquire and assign patients into treatment arms during the trial. Then, we propose strategies for implementing RFAN based on causal, deep Bayesian active learning. Finally, we empirically evaluate the performance of our framework using synthetic and real-world semi-synthetic datasets.

Problem

• Integrate regulatory constraints and treatment policy value
• Address treatment effectiveness across diverse populations
• Design adaptive clinical trials using machine learning

Innovation

• Randomize First Augment Next (RFAN) framework
• Causal, deep Bayesian active learning
• Synthetic and real-world semi-synthetic datasets