This project aims to systematically investigate approaches to detect underlying missingness mechanisms, compare imputation approaches and showcase sensitivity analyses to build confidence in pharmacoepidemiological analyses with partially observed confounder variables. Special emphasis will be given to informative missingness in the context of studying causal treatment effects in Electronic Health Records (EHR) and EHR-linked databases. The overall goal of this proposal is to develop standardized “toolkits” that can be readily implemented in EHRs to describe and, when assumptions permit, address missingness in confounding variables.
Rishi Desai, MS, PhD; Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
Janick Weberpals, PhD; Robert Glynn, PhD, ScD; Luke Zabotka, BA; Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
Sudha Raman, PhD; Brad Hammill, PhD; Department of Population Health Sciences, Duke University of School of Medicine, Raleigh, NC
Elizabeth Messenger-Jones, MSPH; Darren Toh, ScD; John Connolly, ScM, ScD; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA
Pamela Shaw, PhD, MS; Kaiser Permanente Washington Health Research Institute, Seattle, WA
Fang Tian, MPH, MHS, PhD; Wei Liu, PhD; Jenni Li, PhD; Jose Hernandez, RPh, MPH, MSc, PhD; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD
Hana Lee, MS, PhD; Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD