Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions
This work addresses the problem of making clinical trials more efficient and equitable for regulatory bodies and patients, though it is incremental as it builds on existing adaptive trial methods.
The paper tackles the challenge of designing clinical trials that meet regulatory standards while optimizing treatment assignment for diverse populations, introducing the RFAN framework which combines randomization with adaptive learning and shows improved efficiency in synthetic and real-world evaluations.
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.