Michael Harhay, PhD, MPH

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Michael Harhay, PhD, MPH

Assistant Professor of Epidemiology, Medicine (Pulmonary and Critical Care), and Statistics and Data Science

Dr. Michael Harhay specializes in the statistical design, analysis, and interpretation of large randomized trials and cohort studies. At Penn, he oversees a broad research portfolio, including serving as the primary investigator of a PCORI cluster trial methodology award, and MPI of a NHLBI R01 developing Bayesian causal inference methods for ARDS trials, a NIA R01-funded hybrid type I pragmatic cluster trial to improve hospital palliative care delivery, and of Penn’s NHLBI/NIGMS ARDS, Pneumonia, and Sepsis (APS) Phenotyping Consortium site (U01-HL168419). Dr. Harhay is particularly interested in embedded-health system studies, leading to current roles as Director of Statistical Evaluation for the Penn Medicine Nudge Unit at Penn’s Center for Health Care Transformation and Innovation, and Co-Director of the Research Data and Analysis Core of Penn’s AHRQ/PCORI-funded learning health system science program, Penn PORTAL

Dr. Harhay has authored more than 200 scientific publications and is involved in a wide range of international research activities, including randomized trial data safety and monitoring boards, societal and trial steering committees, research consortia, and currently serves as deputy editor of the American Journal of Respiratory and Critical Care Medicine and is an editor of the International Journal of Epidemiology. He is the recipient of several teaching and research awards, including the 2021 Assembly on Critical Care Early Career Achievement Award from the American Thoracic Society and the 2023 Early Career Epidemiologist Award from the American College of Epidemiology. In 2023, he was named an Honorary Research Fellow at the Institute of Clinical Trials and Methodology at University College London.

Content Area Specialties

Critical care, pulmonary, and cardiovascular medicine; analysis and design of randomized trials; solid-organ transplantation

Methodology Specialties

Clinical epidemiology, medical statistics (prediction modeling, clustered and multilevel data, time-to-event/survival analysis); informatively missing and truncated data; econometrics and policy evaluation

About Us

To understand health and disease today, we need new thinking and novel science —the kind  we create when multiple disciplines work together from the ground up. That is why this department has put forward a bold vision in population-health science: a single academic home for biostatistics, epidemiology and informatics. 

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