L.J. Wei is a professor of Biostatistics at Harvard University. Before joining Harvard, he was a professor at University of Wisconsin, University of Michigan, and George Washington University. His main research interest is in the clinical trial methodology, especially in design, monitoring and analysis of studies. He has developed numerous novel statistical methods which are utilized in practice. He received the prestigious Wald Medal in 2009 from the American Statistical Association for his contribution to clinical trial methodology.

Iván Díaz, Ph.D., is an Assistant Professor of Biostatistics at Weill Cornell Medicine. He completed his Ph.D. in Biostatistics at UC Berkeley and was a postdoctoral fellow at Department of Biostatistics in The Johns Hopkins Bloomberg School of Public Health. His research focuses on the study of statistical methods for causal inference from observational and randomized studies with complex datasets. He works at the intersection of causal inference, machine learning, and mathematical statistics to develop methods that provide relevant answers to substantive questions using state-of-the-art data analysis techniques.

Rod Little is Richard D. Remington Distinguished University Professor of Biostatistics at the University of Michigan, where he also holds appointments in the Department of Statistics and the Institute for Social Research. He chaired the Biostatistics Department at Michigan for 11 years. He has over 250 publications, notably on methods for the analysis of data with missing values and model-based survey inference, and the application of statistics to diverse scientific areas, He chaired an influential National Research Council study on the treatment of missing data in clinical trials.

Dr. G. Frank Liu is a distinguished scientist at Merck & Co., Inc. and a Fellow of the American Statistical Association. For more than 26 years at Merck, Frank has worked on various therapeutical areas and conducted research in longitudinal trials, missing data, noninferiority trials, and Bayesian methods; and served as a technical consultation and leading the development of many methodological guidance documents. Before joining Merck, he received his PhD in statistics from UCLA and completed a post-doc in Biostatistics at Johns Hopkins University.

Dr. Fang Chen is Director of Analytical Software Development at SAS Institute Inc. and a Fellow of the American Statistical Association. He manages the development of statistical software for SAS/STAT®, SAS/QC®, and analytical components that drive SAS® Visual Statistics software. Also among his responsibilities is the development of Bayesian analysis software and the MCMC procedure. Before joining SAS, he received his PhD in statistics from Carnegie Mellon University.

Stef van Buuren is Professor of Statistical Analysis of Incomplete Data at the University of Utrecht and Principal Scientist at the Netherlands Organisation for Applied Scientific Research TNO in Leiden. His interests include the analysis of incomplete data, child growth and development, computational statistics, measurement and individual causal effects. Van Buuren is the inventor of the MICE algorithm for multiple imputation of missing data. He created the growth charts used in the Dutch child health care system, and designed the D-score, a new system for expressing child development on a quantitative scale. He consults for the World Health Organization and the Bill & Melinda Gates Foundation.

Gerko Vink is a statistician masquerading as a data scientist with a passion for educating people. He aims to be at the cutting edge of both teaching and research and has an interest in new developments concerning the presentation of data, results and knowledge. Gerko is Associate Professor of Applied Data Science at Utrecht University (Utrecht, Netherlands) where his research and teaching focuses on incomplete data problems, computational evaluation and programming

Danyu Lin, Ph.D., is the Dennis Gillings Distinguished Professor of Biostatistics at the University of North Carolina at Chapel Hill. Dr. Lin is an internationally recognized leader in lifetime data analysis and currently serves as an Associate Editor for Biometrika (since 1997) and JASA. He has published over 200 peer-reviewed papers, most of which appeared in top statistical journals. Several of his methods have been incorporated into major software packages, such as SAS, R and STATA, and widely used in practice.

Dr. Hubbard is a Professor of Biostatistics in the Department of Biostatistics, Epidemiology and Informatics at the University of Pennsylvania. Her research focuses on development and application of statistical methodology for studies using data from electronic health records (EHR). This work encompasses evaluation of screening and diagnostic tests, methods for comparative-effectiveness studies, and health services research.

Keaven Anderson, PhD, is an Associate Scientific VP of Methodology Research at Merck focused on late-stage statistical design and analysis. Keaven is a Fellow of the American Statistical Association. He has a long-standing interest in methodology, including survival analysis, group sequential design and multiplicity. He is the primary author of the gsDesign R package for group sequential design. While he has extensive experience in many therapeutic areas, his focus has been in oncology for the last 10+ years.

Yilong Zhang is a statistician from Merck. He is working with a group of statisticians and programmers to demonstrate the capability of using R for regulatory work. Other research interests include statistical methods in study design, missing data, and survival analysis. Before joining Merck, he earned Ph.D. degree in Biostatistics at New York University

Nan Xiao (Ph.D. in statistics from Central South University, China). Nan is an Associate Principal Scientist in Methodology Research at Merck Research Laboratories. His research interests include sparse linear models, representation learning, and computational reproducibility. He received the John M. Chambers Statistical Software Award from the American Statistical Association in 2018. His current focus is on innovative design and analysis for clinical studies through statistical methods development and robust software implementation.

Dr. Jeffrey Wilson is a Professor of Statistics and Biostatistics at Arizona State University. Dr. Wilson’s research experience includes grants as PI and co-PI from the NIH, NSF, USDA, Arizona Department of Health Services, and the Arizona Disease Research Commission. He is presently the Statistics Associate Editor for The Journal of Minimally Invasive Gynecology and a former Chair of the Editorial Board of the American Journal of Public Health. He has published more than 85 articles in leading journals such as Statistics in Medicine, American Journal of Public Health, Journal of Royal Statistics Society, Computational Statistics, and Australian Journal of Statistics, among others.

Dr. Din Chen is now the Wallace H. Kuralt distinguished professor in Biostatistics at the University of North Carolina-Chapel Hill. Before this, Dr. Chen was a professor in biostatistics at the University of Rochester Medical Center, the Karl E. Peace endowed eminent scholar chair and professor in biostatistics from the Jiann-Ping Hsu College of Public Health at the Georgia Southern University. Dr. Chen is an elected fellow of the American Statistical Association (ASA), an elected member of the International Statistics Institute (ISI) and a senior expert consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trial biostatistics.

1987: Ph.D. (Statistics), University of Wisconsin, Madison, Wisconsin, U.S.A.1982: B.S. (Mathematics), East China Normal University, Shanghai, China

Aug. 1996{present: Professor, University of Wisconsin
Jan. 1994{Aug. 1996: Associate Professor, University of Wisconsin
May 1991{Dec. 1993: Associate Professor, University of Ottawa
Sep. 1989{May 1991: Assistant Professor, University of Ottawa
Aug. 1987{Mar. 1989: Visiting Assistant Professor, Purdue University
MAJOR ADMINISTRATIVE WORK AT UW-MADISON
Aug. 1997|July 2004: Associate Chair of Department of Statistics
July 2005|July 2009: Chair of Department of Statistics

Professor Evans interests include the design, monitoring, analyses, and reporting of and education in clinical trials and diagnostic studies. He is the author of more than 150 peer-reviewed publications and three textbooks on clinical trials including Fundamentals for New Clinical Trialists. He is the Director of the Statistical and Data Management Center (SDMC) for the Antibacterial Resistance Leadership Group (ARLG), a collaborative clinical research network that prioritizes, designs, and executes clinical research to reduce the public health threat of antibacterial resistance.

Toshimitsu Hamasaki is a Research Professor of the George Washington University (GWU) Biostatistics Center and the Department of Biostatistics and Bioinformatics. Prior to joining GWU, he worked at Shiogoni, Pfizer, Osaka University and National Cerebral and Cardiovascular Center. His research interests include the design, monitoring, analyses, and reporting of clinical trials. He is the author of more than 200 peer-reviewed publications and four textbooks on statistical methods in clinical trials including “Sample Size Determination in Clinical Trials with Multiple Endpoints” and “Group-Sequential Clinical Trials with Multiple Co-Objectives

Divan A. Burger (Ph.D. in Mathematical Statistics from University of Free State) obtained BCom (Actuarial Science), BCom (Hons) (MathematicalStatistics) and MCom (Mathematical Statistics) degrees at the University of the Free State (UFS) in 2006, 2007 and 2009. He completed his PhD studies (2013 to 2014) under the supervision of Profs Robert Schall and Abrie van der Merwe at UFS. He previously worked at Quintiles Biostatistics in Bloemfontein (2007 to 2016) as a senior biostatistician, where he specialized in the planning, statistical analysis and reporting of clinical trial data. He was a postdoctoral research fellow at the Department of Mathematical Statistics and Actuarial Science at the University of the Free State (2015 to 2016), where his primary research areas include Bayesian mixed-effects nonlinear regression analysis.

Dong Xi (Ph.D. in Statistics from Northwestern University) is Associate Director in Advanced Methodology and Data Science group at Novartis. He has supported development and implementation of innovative statistical
methodologies in multiple comparisons, dose finding, group sequential designs, estimands and causal inference. He has co-authored four book chapters on multiplicity and many publications in peer-reviewed journals.
He is an associate editor of Statistics in Biopharmaceutical Research and Contemporary Clinical trials, and he is a committee member of the International Conference of Multiple Comparison Procedures. His work won
the biennial (2019-2020) “Best Paper Award” for manuscripts published in Statistics in Biopharmaceutical Research.

Frank Bretz (Ph.D. in Statistics from Leibniz Universität Hannover) is a Distinguished Quantitative Research Scientist at Novartis. He has supported the methodological development in various areas of pharmaceutical
statistics, including adaptive designs, dose finding, estimands, and multiple testing. He currently holds adjunct professorial positions at the Hannover Medical School (Germany) and the Medical University of Vienna
(Austria). He was a member of the ICH E9(R1) Expert Working Group on ‘Estimands and sensitivity analysis in clinical trials’ and currently serves on the ICH E20 Expert Working Group on ‘Adaptive clinical trials’. He
is a Fellow of the American Statistical Association.

Wa lt Stroup(Ph.D. in Statistics from University of Kentucky) is Emeritus Professor of Statistics at the University of Nebraska-Lincoln. He served on the University of Nebraska faculty from 1979 until 2020. His
responsibilities included teaching statistical modeling, design of experiments, and research specializing in mixed models andtheir applications in agriculture, natural resources, medical and pharmaceutical sciences,
education, and the behavioral sciences. He is the founding chair of Nebraska’s Department of Statistics andserved as chair from 2001 until 2010. In 2020, he received the University of Nebraska’s Outstanding Teaching
and Innovative Curriculum Award, the university’s highest teaching honor.

Xu Shi (Ph.D. in Biostatics from University of Washington) is an Assistant Professor in the Department of Biostatistics at University of Michigan. Her research focuses on developing novel statistical methods that provide
insights from high volume and high variability administrative healthcare data such as the electronic health records (EHR) data. She develops scalable and automated pipelines for curation and harmonization of EHR data across healthcare systems. She also develops causal inference methods that harness the full potential of EHR data to address comparative effectiveness and safety questions.

Li Qian Helen(Ph.D. in Biostatistics from Harvard University) has over 20 years of experience in the field of clinical statistics and worked on a range of therapeutic areas including oncology, cardiovascular, pulmonary,
pain, ophthalmic, anti-inflammatory and anti-infective drug products. Her publications cover innovative statistical methods including the area of multiplicity and survival analyses. She currently works for Bristol Myers
Squibb and was an experienced statistical reviewer in FDA and NIH. She received her doctoral degree in Biostatistics from Harvard School of Public Health, had a master degree from Purdue University and undergraduate
degree from Tsinghua University

Ting Ye is an Assistant Professor in the Department of Biostatistics at the University of Washington. Her research centers around addressing modern complications in randomized clinical trials and hidden biases in causal inference. In randomized clinical trials, she has developed pragmatic and robust statistical methods for delayed treatment effect in cancer immunotherapy trials, survival-time-dependent missing covariates, monotone order constraints in stratified phase II cancer trials, and covariate adjustment under covariate-adaptive randomization. In causal inference, she has expertise in instrumental variables, difference-in-differences, sensitivity analysis, and data integration