CAREER: Innovative Methods for Designing Adaptive Clinical Trials

Clemson University, Clemson SC

Investigators

Abstract

The goal of this Faculty Early Career Development (CAREER) project is to develop a flexible optimization framework for optimal learning in adaptive design of clinical trials. Adaptive designs have been of great interest since the Federal Drug Administration (FDA) approved the concept and initiated pilot studies in 2004. Unlike fixed clinical trials, where sample sizes are determined in advance and conclusions made only at the end of the trial, adaptive clinical trials allow for interim modifications in assigning patients to treatment groups, adjusting of dosage levels, or terminating trials based on developing evidence of success or failure. Adaptive trials hold the promise of improving safety of new treatments, reducing time to market of efficacious treatments, and limiting exposure to inferior treatments. Moreover, adaptive clinical trials can more easily incorporate patient heterogeneity (e.g., based on biomarkers or individual exposure), which may lead to more effective, personalized treatments for particular patient subsets. The educational plan will incorporate adaptive design concepts in undergraduate and graduate coursework as a novel means of introducing optimal learning techniques. In addition, the project will provide educational seminars on adaptive methods to physicians, nurses, and other healthcare practitioners who are conducting clinical trials through two collaborating healthcare systems. This CAREER project will advance knowledge by establishing unifying solution frameworks to two classes of optimal learning problems, namely (i) ranking and selection problems with arbitrary (possibly correlated) belief distribution and the objective of learning a population with a desired property, and (ii) multiarmed bandit problems with correlated rewards and the dual objective of learning the best population and maximizing the total reward, where at each period any subset of arms can be chosen any number of times subject to a budget on the total number of pulls. Novel approximate dynamic programming methods integrated with Bayesian statistics are employed to study the solution space and establish bounds based on duality theory that will assess the quality of solutions. The results will shed light on the role that patient heterogeneity plays in adaptive clinical trial design and should extend to other applications in optimal learning, such as dynamic pricing, revenue management, and assortment planning.