Invited Session 3: Bridging Evidence and Meaning: Statistical Approaches to Clinical Significance
1:30 PM - 3:15 PM, May 16, 2025; Chem-H Rotunda E241
Chair: Ming-Hui Chen, UConn
Panel: May Mo, Yuan Ji, Steven Goodman, Lee-Jen Wei, Kevin Bugin
Chair: Ming-Hui Chen, UConn
Dr. Ming-Hui Chen is a Board of Trustees Distinguished Professor and Head of Department of Statistics at University of Connecticut (UConn). He was elected to Fellow of American Association for the Advancement of Science (AAAS) in 2024, Fellow of International Society for Bayesian Analysis in 2016, Fellow of Institute of Mathematical Statistics in 2007, and Fellow of American Statistical Association in 2005. He received the University of Connecticut AAUP Research Excellence Award in 2013, the UConn College of Liberal Arts and Sciences (CLAS) Excellence in Research Award in the Physical Sciences Division in 2013, the University of Connecticut Alumni Association's University Award for Faculty Excellence in Research and Creativity (Sciences) in 2014, the ICSA Distinguished Achievement Award in 2020, and the Distinguished Science Alumni Award from Purdue University in 2023. He has published 460+ peer-reviewed journal articles and five books including two advanced graduate-level books on Bayesian survival analysis and Monte Carlo methods in Bayesian computation. He has supervised 42 PhD students. He served as, President of ICSA (2013), Chair of the Eastern Asia Chapter of International Society for Bayesian Analysis (2018), President of New England Statistical Society (2018-2020), and the 2022 JSM Program Chair. Currently, he is Co Editor-in-Chief of Statistics and Its Interface, inaugurated Co Editor-in-Chief of New England Journal of Statistics in Data Science, and an Associate Editor for several other statistical journals.
Panelist: Steven Goodman, Stanford University
Steven Goodman, MD, MHS, PhD, is Associate Dean for Clinical and Translational Research and Professor of Epidemiology, Medicine and Health Policy (by courtesy) at the Stanford University School of Medicine. He founded and directs the Stanford Program on Research Rigor and Reproducibility (SPORR), and is co-founder and co-director of the Meta-research Innovation Center at Stanford (METRICS). He teaches a variety of courses on statistical inference and biomedical research methods.
His research interests are in scientific and statistical inference, research reproducibility and meta-research, and their implications for research ethics and policy. He was vice-chair, then chair of the PCORI Methodology Committee from 2013-2024, has been senior statistical editor at the Annals of Internal Medicine for over three decades, was the editor of Clinical Trials: Journal of the Society for Clinical Trials, from 2003-2013, is on the PNAS statistical review team, and is scientific advisor to the national Blue Cross-Blue Shield technology assessment program. Before coming to Stanford in 2011, Steve was a member and then director of the Johns Hopkins Kimmel Cancer Center’s Division of Biostatistics and Bioinformatics, from 1990-2010.
Dr. Goodman drafted the ASA’s 2016 P-value statement and gave a keynote address entitled “Why is getting rid of P-values so hard? Musings on science and statistics” at their 2017 colloquium on statistical inference themed “Beyond P<0.05”. He was awarded the 2016 Spinoza Chair in Medicine from the University of Amsterdam for his work in inference, the 2019 Lilienfeld award from the American College of Epidemiology for his lifetime contributions to the field and is an elected member of the National Academy of Medicine.
Panelist: Yuan Ji, University of Chicago
Dr. Yuan Ji is Professor of Biostatistics at The University of Chicago. His research focuses on innovative Bayesian statistical methods for translational cancer research. Dr. Ji is author of over 170 publications in peer-reviewed journals including across medical and statistical journals. He is the inventor of many innovative Bayesian adaptive designs such as the mTPI and i3+3 designs, which have been widely applied in dose-finding clinical trials worldwide. His work on cancer genomics has been reported by a large number of media outlets in 2015. He received Mitchell Prize in 2015 by the International Society for Bayesian Analysis. He is an elected fellow of the American Statistical Association.
Panelist: Lee-Jen Wei, Harvard University
L.J. Wei is a professor of Biostatistics at Harvard University. Before joining Harvard, he was a professor at the University of Wisconsin, University of Michigan, and George Washington University. His main research interest is in clinical trial methodology, especially in design, monitoring and analysis of studies. He has developed numerous novel statistical methods which are utilized often in practice. He received the prestigious Wald Medal in 2009 from the American Statistical Association for his contribution to clinical trial methodology. He is a fellow of American Statistical Associating and Institute of Mathematical Statistics. In 2014, to honor his mentorship, Harvard School of Public Health established a Wei-family scholarship to support students studying biostatistics. His recent research area is concentrated on translational statistics, personalized medicine under the risk-benefit paradigm via biomarkers and revitalizing clinical trial methodology. He has more than 280 publications and serves numerous editorial and scientific advisory boards including data monitoring for governments and industry. He has extensive working experience in regulatory science for developing and evaluating new drugs/devices.
Panelist: May Mo, Amgen
May Mo is an Executive Director of Biostatistics at Amgen. She is the Head of Design and Innovation (D&I) group in the Center for Design and Analysis, and the Chair of Innovative Clinical Trial Design Council within Amgen Global Development. In her roles, May has been a strong advocate and enabler of innovative approaches in clinical development planning and clinical trial design. As the results of her team’s effort and in collaboration with many cross-functional colleagues, a great majority of new clinical trials within Amgen have taken innovative approaches (adaptive designs, Bayesian methods, master protocols, external control, etc.) into consideration, and leveraged modeling and simulation to guide the design option evaluation and optimization. May has over 20 years of drug and device development experience in Amgen and Abbott laboratory. She completed a master’s degree, post-master research and PhD courses in Statistics at the University of Chicago and a Master’s in Business Administration from California Lutheran University.