Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions
Journal:
arXiv
Published Date:
Mar 12, 2025
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.
