(September 24, 2026) — The Biostatistics Center (BSC) is proud to highlight the accomplishments of our faculty and students who are advancing the frontiers of clinical trials, regulatory science, and statistical methodology.
Explore our featured updates, including research presentations by Professor Scott Evans and PhD candidates Shanshan Zhang, Qihang Wu, and Lizhao Ge at the 2026 Regulatory-Industry Statistics Workshop (RISW), as well as Qihang Wu’s selection for the prestigious FDA-OCE-ASA Oncology Educational Fellowship.
2026 Regulatory-Industry Statistics Workshop (RISW)
Professor Scott Evans presented an invited talk “Preparing the Next Generation: Career Development of Young Statisticians” at the 2026 Regulatory-Industry Statistics Workshop (RISW). His talk highlighted that statistics is central, not auxiliary, to every science, and that learning statistics is one thing, but learning to be a statistician is another. He emphasized the importance of understanding the research question and ensuring that it is the right one, preserving evidentiary standards and scientific integrity, developing effective communication skills, of never-ending education, perseverance, and courage during challenging times for research careers.
Three PhD students in the Biostatistics Track of the Health Data Science program, who are affiliated with the Biostatistics Center and the Antibacterial Resistance Leadership Group (ARLG), presented their work at the 2026 American Statistical Association (ASA) Biopharmaceutical Section Regulatory-Industry Statistics Workshop (RISW), held September 16–18 in Rockville, Maryland.
Shanshan Zhang presented “Tools for Pragmatic Evaluation and Comparison of Interventions and Diagnostics.” Her presentation highlighted four free, online R Shiny tools developed by the ARLG research team: (1) Desirability of Outcome Ranking (DOOR), (2) Benefit-Risk Evaluation for Diagnostics: a Framework and Average Weighted Accuracy (BED-FRAME/AWA), (3) Intention-to-Diagnose (ITD), and (4) DOOR for the Management of Antimicrobial Therapy (DOOR-MAT). These tools translate published statistical methodologies into practical analysis workflows for clinical research. Her presentation, part of a session focused on regulatory science tools, highlighted her ongoing efforts to make innovative statistical methods more accessible and useful to clinical researchers.
Qihang Wu presented “Selecting Good Treatments in Multi-Arm Clinical Trials with Continuous Outcomes: Potential Extensions to DOOR and Multistage Designs.” His presentation described statistical methods for selecting good treatments in multi-arm clinical trials. The work formalizes good-treatment selection as retaining all treatments within a prespecified tolerance of the best treatment, with a guaranteed probability of retaining all such treatments. It places single-step and stepwise selection procedures within a common framework linking treatment retention to multiple-testing error control, evaluates their ability to retain good treatments while excluding inferior treatments, and considers future extensions to DOOR outcomes and multistage designs. The work addresses methodological questions relevant to treatment selection in multi-arm and multistage clinical trials with continuous or ordinal outcomes.
Lizhao Ge presented “Benefit-Risk Assessment in Clinical Trial Monitoring Using a Repeated Prediction Interval Approach.” Her work extends prediction-based methods using the repeated confidence interval framework for interim monitoring of clinical trials. The proposed approach uses the DOOR methodology to evaluate benefit-risk and provide quantitative information to support Data Monitoring Committee (DMC) decision-making. She illustrated the approach using repeated predicted confidence interval (RPCI) plots, which visually display the projected between-treatment contrast and its associated uncertainty as a trial continues, facilitating interpretation of potential future trial outcomes by DMC members.
FDA-OCE-ASA Oncology Educational Fellowship
Qihang Wu, a biostatistician with the Biostatistics Center and the Antibacterial Resistance Leadership Group (ARLG), and a PhD candidate in the Biostatistics Track of the Health Data Science program, has been selected as an FDA-OCE-ASA Oncology Educational Fellow.
The FDA-OCE-ASA Oncology Educational Fellowship is a collaborative program developed by the American Statistical Association (ASA) in partnership with the FDA Oncology Center of Excellence (OCE) and the ASA Biopharmaceutical Statistics Section. The fellowship is designed for advanced statistics and biostatistics PhD students and postdoctoral fellows who are interested in deepening their understanding of oncology drug development and regulatory science.
Through this fellowship, Qihang will explore advanced topics in oncology medical product development, including oncology endpoint selection and assessment, multiplicity considerations, adaptive design methodologies, subgroup analyses, regulatory science, and the evolving role of statisticians in drug development.