This project will develop a causal inference framework for safety and effectiveness evaluations of medical products that leverage claims data alone or in combination with electronic health record data. The framework will consider the domains of study conduct including transparency, study implementation, and robustness evaluation. This framework can be used as a guide for the design and conduct of an inferential study, or to efficiently evaluate studies implemented by others. This project will demonstrate the value and utility of the proposed framework by implementing these principles in one or more case-example(s) using routinely collected healthcare data.
Rishi Desai, Ph.D.; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston MA
Sebastian Schneeweiss, MD, ScD; Shirley Wang, PhD, ScM; Richard Wyss, PhD, MSc; Elisabetta Patorno, MD, DrPH; Sushama Sreedhara, MBBS, MSPH; Luke Zabotka, BA; Shamika More, MS; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
Patrick Heagerty, PhD; Department of Biostatistics at the University of Washington, Seattle, WA
Jennifer Clark Nelson, PhD; Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, WA
Darren Toh, ScD; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA
Xu Shi, PhD; Department of Biostatistics, University of Michigan, Ann Arbor, MI
Sarah Dutcher, PhD, MS; Jie (Jenni) Li, PhD; Dinci Pennap, PhD, MPH, MS; Christina Greene, PhD; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD