Alexis Ogdie-Beatty, MD, MSCE
Dr. Ogdie is Associate Professor of Medicine and Epidemiology in the Perelman School of Medicine. She is also Director of the Center for Clinical Epidemiology and Biostatistics and Director of the Penn Psoriatic Arthritis and Spondyloarthritis Program. Dr. Ogdie's research program focuses on pharmacoepidemiology and observational studies of psoriatic arthritis, an inflammatory arthritis that affects approximately 25% of patients with psoriasis and approximately 500,000 Americans. The mission of her research program is to improve outcomes in psoriatic arthritis by accelerating diagnosis, increasing the focus on meaningful, patient-centered outcomes, and developing and advancing methods for precision medicine.
Areas of expertise include epidemiology and pharmacoepidemiology, biostatistical methods for observational studies (e.g., time to event modeling, prediction modeling), outcome measures (e.g., qualitative and quantitative/psychometric assessment), clinical trial design and more general qualitative methods (e.g., survey, focus group and interview studies). Her recent work has focused on pragmatic trial design in PsA, which is the basis for an R01 funded by NIAMS (R01 AR072363) and trial simulation studies to inform pragmatic trial design in collaboration with statistical collaborator, Alisa Stephens. She is currently co-leading a trial to examine the impact of dietary interventions on psoriatic arthritis disease activity and conducted a pilot trial of physical activity in inflammatory arthritis. Both trials employ concepts from behavioral economics to enhance the effectiveness of the interventions. Finally, a recent focus of her research group has been the early identification of PsA through better understanding predictors of disease that can be implemented in the electronic medical record and design of a trial to test prevention of PsA through treatment of psoriasis.
Content Area Specialties
Disease registries and cohort studies
Randomized Controlled Trials
Measurement of disease outcomes
Prediction of disease