Yale University-Mayo Clinic CERSI

Project Title: New while-alive regression models for analyzing composite survival endpoints.

Dr. Fang is a postdoctoral associate at the Yale School of Public Health, Biostatistics Department, within the Center for Methods in Implementation and Prevention Science. Her research interests encompass survival analysis and composite endpoint analysis in clinical trials. As a CERSI Scholar, her project focuses on developing a while-alive regression method for composite endpoints using spline techniques. This approach enables the estimation of dynamic effects and is applied to clinical trial data.

Project Title: Global Impact of FDA Drug Approval, Major Postmarket Labeling Change, and Withdrawal Determinations.

Dr. Fu is a postdoctoral fellow in the Yale School of Medicine. She obtained her PhD from the School of Pharmaceutical Sciences at Peking University, where her research focused on evaluating equitable access to and the appropriate use of medicines in primary care. As a visiting scholar at Harvard Medical School, she expanded her expertise by investigating evidence-based approval processes and market access for novel cancer drugs in the United States. Her CERSI Scholar project seeks to explore the global impact of FDA regulatory decisions.

Project Title: Confirmatory Evidence Supporting Single Pivotal Trial New Drug Approvals by the Food and Drug Administration, 2015 and 2023.

Carla is a second-year medical student at Yale School of Medicine. Her clinical interests center on pediatrics and hematology/oncology, and her research interests focus on the FDA drug approval process. Her CERSI scholars project characterizes the confirmatory evidence used to support drug approvals based on a single pivotal trial, rather than the standard two pivotal trials.

Project Title: Therapeutic Benefit of the Most Expensive Drugs Covered by Medicare and Medicaid.

Tiffany Jiang is a medical student at the Yale School of Medicine. Her research interests include drug pricing and equitable healthcare access. Her CERSI Scholars project investigates therapeutic value ratings of drugs covered by the CMS and implications for price negotiation policies.

Project Title: Surveying AI Expert Attitudes on Regulation of Artificial Intelligence Tools in Medicine

Dr. Frank Lee is a general surgery resident in the Clinician Investigator Training Program (CITP) at Mayo Clinic in Rochester, MN. He is a member of the Surgical AI2 lab, led by Drs. Hojjat Salehinejad and Cornelius Thiels. His research focuses on AI applications within surgery. Previous work includes AI for detection of surgical site infections and testing large language models (LLMs). His CERSI scholar project seeks to better understand the continuously evolving landscape of AI and elicit perspectives on what is the best way to regulate AI tools in medicine from researchers developing AI and clinicians who will use these tools.

Project Title: Co-pilot Diagnostic Tools for Clinical Trials of Anti-Fibrotic Crohn’s Disease Therapeutics

Garrett Regan is a biomedical engineering doctoral candidate in Mayo Clinic’s Graduate School of Biomedical Sciences. His research interests include uncertainty aware medical image segmentation techniques, and the use of artificial intelligence to improve the reliability of quantitative imaging biomarkers. His CERSI scholar project will incorporate these interests by developing an artificial intelligence co-pilot diagnostic tool for reproducible intestinal stricture localization in clinical trials of novel Crohn’s Disease therapeutics.

Project Title: IV Hydration Spas: State Policies and Facility Practices in the U.S.

Anishaa Sivakumar is an MD/MBA candidate at the Yale School of Medicine and the Yale School of Management. She is pursuing a residency in Internal Medicine, and her research interests include healthcare delivery reform and investigating policy design and regulation in healthcare. Her CERSI Scholars project is focused on assessing the policy landscape and regulation of intravenous hydration spas at the state level.

Project Title: Partial Principal Ignorability (PPI) assumption with a novel odds ratio sensitivity parameter to point-identify PCEs.

Jiaqi Tong is a second-year Ph.D. student in the Department of Biostatistics at the Yale School of Public Health. His research leverages modern statistical tools, including causal machine learning and semiparametric inference, to tackle complex causal inference challenges in real-world settings, with a focus on applications in clinical trials. As a CERSI Scholar, his project focuses on developing novel machine learning methods to address intercurrent events under principal stratification.