Special Seminar – Ethan Fang

Title – Novel Optimization for Data-Driven Decision Making
Abstract
We present several exciting works on data-driven decision making. First, during the crisis of COVID-19, we have seen the importance of clinical trial designs. Improving clinical trial designs is important for the wellness of all human beings. We first present a novel optimization framework for adaptive trial design in the context of personalized medicine. Adaptive enrichment designs involve preplanned rules for modifying enrollment criteria based on accruing data in a randomized trial. We focus on designs where the overall population is partitioned into two predefined subpopulations, e.g., based on a biomarker or risk score measured at baseline for personalized medicine. The goal is to learn which populations benefit from an experimental treatment. Two critical components of adaptive enrichment designs are the decision rule for modifying enrollment and multiple testing procedures. We provide a general framework for simultaneously optimizing these components for two-stage, adaptive enrichment designs through Bayesian optimization. We minimize the expected sample size under constraints on power and the familywise Type I error rate. It is computationally infeasible to directly solve this optimization problem due to its nonconvexity and infinite dimensionality. The key to our approach is a novel, discrete representation of this optimization problem as a sparse linear program, which is large-scale but computationally feasible to solve using modern optimization techniques. Applications of our approach produce new, approximately optimal designs. We then present some other optimal data-driven decision making works on high-dimensional linear contextual bandit and reinforcement learning problems.