Dr. Haoda Fu, Head of Exploratory Biostatistics at Amgen, will be speaking about "Under the Hood of Multiplicity Control: From Closed Testing to Simulation-Optimized Graphs" on Wednesday, May 13th, 2026 from 3:30 - 4:30pm in HSSB 1173.
Abstract:
Multiplicity control in clinical trials is often introduced as a list of procedures, but the deeper issue is how to make valid, transparent decisions when many claims are possible. This talk goes under the hood of strong family-wise error rate control for primary and key secondary endpoints, contrasting weak and strong control and showing how closed testing provides the theoretical foundation. We then translate this theory into graphical procedures, where alpha allocation and transfer rules make the testing strategy explicit, auditable, and aligned with clinical priorities. Through a Study XYZ-style example, we show how a prespecified graph can preserve valid claim opportunities that rigid gatekeeping would block, without turning the analysis into a post hoc rescue. The talk also introduces simulation-based graph evaluation, including how to compare candidate graphs by claim probability, utility, robustness, and operating characteristics across plausible scenarios. The broader message is that good analytics is not an isolated method; it is a framework for bringing Statistics, Medicine, Regulatory, and Commercial expertise into one robust design decision. By the end, participants should understand not only what graphical multiplicity control does, but why it works, how to evaluate it, and how it can create more credible opportunities for claimable evidence.
Bio:
Dr. Haoda Fu is Head of Exploratory Biostatistics in Amgen, before that he was an Associate Vice President and an Enterprise Lead for Machine Learning, Artificial Intelligence, from Eli Lilly and Company. Dr. Haoda Fu is a Fellow of ASA (American Statistical Association), and IMS Fellow (Institute of Mathematical Statistics). He is also an adjunct professor of biostatistics department, Univ. of North Carolina Chapel Hill and Indiana university School of Medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin - Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very
active in statistics and data science methodology research. He has more than 100 publications in the areas, such as Bayesian adaptive design, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and rare events analysis. In recent years, his research area focuses on machine learning and artificial intelligence. His research has been published in various top journals including JASA, JRSS-B, Biometrika, Biometrics, ACM, IEEE, JAMA, Annals of Internal Medicine etc.. He has been teaching topics of machine learning and AI in large industry conferences including teaching this topic in FDA workshop. He was board of directors for statistics organizations and program chairs, committee chairs such as ICSA, ENAR, and ASA Biopharm session. He is a COPSS Snedecor Awards committee member from 2022-2026, and also served as an associate editor for JASA theory and method from 2023, and JASA application and case study from 2025-2027.