University of Memphis Zhili Tian: Optimal Enrollment Strategies for Group Sequential Clinical Trials in Late-Stage Drug Development Based on Learning Drug Efficacy
October 16, 2026
Topic:Optimal Enrollment Strategies for Group Sequential Clinical Trials in Late-Stage Drug Development Based on Learning Drug Efficacy
Speaker:Associate Professor Zhili Tian, Fogelman College of Business and Economics, University of Memphis
Moderator:Associate Professor Chen Qiong, Institute of Economic and Management, Southwestern University of Finance and Economics
Time:October 19 (Monday), 10:30-12:00, 2026
Venue:Room 1211, Gezhi Building, Liulin Campus, Southwestern University of Finance and Economics
Organizer:Institute of Economic and Management, Southwestern University of Finance and Economics
Speaker's Biography:
Dr. Zhili Tian is currently the Dunavant Associate Professor in the Department of Supply Chain Management at the Fogelman College of Business and Economics, University of Memphis. He holds a Bachelor of Science in Civil Engineering from Tsinghua University, a Master of Science in Transportation from the Massachusetts Institute of Technology, a Master of Business Administration from the Anderson School of Management at the University of California, Los Angeles, and a Ph.D. in Supply Chain, Operations, and Technology from Washington University in St. Louis.
Dr. Tian's research interests span a wide range of areas, including supply chain management, logistics and transportation, healthcare operations, pharmaceutical innovation, new product development strategies, data analytics, and reinforcement learning. His research employs a variety of methodologies and has been published in top-tier journals such as *Manufacturing & Service Operations Management (MSOM), Production and Operations Management (POM), Decision Sciences, European Journal of Operational Research (EJOR), Omega, IISE Transactions, and Journal of the Academy of Marketing Science. One of his collaborative papers received the Best Applied Paper Award in Scheduling and Logistics from IISE Transactions in 2011.
Dr. Tian serves as a reviewer for several top journals, including Management Science, Operations Research, MSOM, and POM. He is also actively involved in organizing special sessions on technology innovation and entrepreneurship management at international academic conferences such as INFORMS and POMS.
Abstract:
Problem Definition.Between 2010 and 2019, the cost of new drug development ranged from $1 billion to over $2 billion. In addition to the high development costs, there are uncertainties associated with the efficacy of candidate drugs, patient enrollment, market exclusivity period (MEP), and the planning time horizon. Slow patient enrollment can lead to increased costs, clinical trial cancellations, and potential revenue losses. Many companies aim to incorporate interim analyses of patient response data in their clinical trials to identify the efficacy or futility of candidate drugs early, thereby reducing development costs.
Academic/Practical Relevance.The problem of optimizing patient enrollment rates is characterized by an uncertain planning time horizon. We develop a continuous-time dynamic programming (DP) model that incorporates learning about drug efficacy and MEP to help companies formulate optimal enrollment strategies for their clinical trials. We also derive the optimality equations for this DP model. Using a case study of a clinical trial for an anticancer drug developed by a leading pharmaceutical company, we demonstrate that our DP model can help companies effectively manage their trials and achieve significant profit gains (up to $270 million per drug). Companies can also use our model in simulations to select trial design parameters, such as the sample size for interim analyses.
Method/Results.We update the drug efficacy using Bayesian rules. By leveraging stochastic and likelihood ratio orders of distribution functions, we prove the monotonic properties of the value function and the optimal strategy. We show that as the average patient response to the candidate drug improves, the value of the drug development project increases. For drugs with lower annual revenues, strong market brands, or those used to treat rare diseases, we also demonstrate that the optimal enrollment strategy is monotonic with respect to the average patient response. Additionally, the optimal enrollment rate increases as the variance of the MEP decreases.
Managerial Implications.Companies can use the properties of the value function to select late-stage clinical trials for their drug development portfolios. They can also employ our optimal strategies to guide patient recruitment in their clinical trials, considering the competition from other drugs on the market.
