Details
Project to evaluate statistical methods to improve automated confounding control in electronic healthcare databases in a range of drug safety scenarios by 1) applying high-dimensional propensity score adjustment to medical product safety surveillance systems, and 2) examining the effect of Z-bias, a type of bias caused by adjusting for an instrumental variable in an analysis.
Additional Information
Contributors
Robert Glynn, PhD, ScD; Brigham and Women’s Hospital Division of Pharmacoepidemiology and Pharmacoeconomics, and Harvard Medical School, Boston, MA
Jeremy A. Rassen, ScD; Jessica Myers, PhD, ScD; Sebastian Schneeweiss, MD, ScD; Joshua J. Gagne, MS, PharmD; Krista F. Huybrechts, MS, PhD; Brigham and Women’s Hospital Division of Pharmacoepidemiology and Pharmacoeconomics, and Harvard Medical School, Boston, MA
Kenneth J. Rothman, MPH, DrPH; RTI International, Research Triangle Park, NC
Marshall M. Joffe, MD, MPH, PhD; University of Pennsylvania School of Medicine, Philadelphia, PA