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Post-doctoral Fellowships in Pharmacoepidemiology:
Sebastian Schneeweiss, MD, MS, ScD
Professor of Medicine, Harvard Medical School
Professor in Epidemiology, Harvard Chan School of Public Health
Division of Pharmacoepidemiology and Pharmacoeconomics
Department of Medicine
Brigham and Women’s Hospital
1 Brigham Circle, Suite 3030, Boston, MA 02120
P: 617 278-0930 M: 617 3312632
October 28, 2020
The Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital Department of Medicine and Harvard Medical School is accepting applications for multiple post-doctoral fellows in pharmacoepidemiology - applied and methodologically focused. The Division includes 25 faculty (drugepi.org/dope/team) and 75 staff who work closely together to research how we use medications effectively and improve health. We are a world-leading interdisciplinary research center that brings together the various specialties of medicine, epidemiology, biostatistics, health services research, legal, regulatory and the social sciences to evaluate the effectiveness of prescription drugs in relation to their risks and costs; to study how medications are prescribed and used; to develop methods to optimize prescription drug use; to understand how medicines are approved and regulated after their marketing. The Division is a first-rank training site for graduate students and fellows in a variety of subject areas and methodological research. We are seeking one or more self-motivated, diligent, and independent fellows to work with Division faculty in one or more of the following areas:
- Developing and implementing cutting-edge methods to bridge the gap between randomized clinical trials (RCTs) and real-world evidence (RWE): RCTs and RWE are critical and complementary sources of evidence generation about the benefits and safety of medical products. A fellow working in this area will be involved in several interrelated projects that will leverage individual-level RCT data to explore this complementarity and will be expected to explore and test novel analytical approaches for analysis of RCT and real-world data. Training and experience in statistical modeling and programing is required. Experience with developing prediction models, model validation and calibration approaches, imputation methods, Monte Carlo simulations, and machine learning algorithms is highly desirable.
- Answering high-impact questions to inform clinical decision making on the comparative effectiveness and safety of medications in cardio-metabolic and renal conditions by applying and advancing cutting edge methods: A fellow working in this area will collaborate closely with Division faculty who are leaders in the pharmacoepidemiology of cardio-metabolic and renal diseases to answer critical clinical questions on the use of medications and their comparative effectiveness and safety leveraging real-world data, including administrative claims, electronic health records, and clinical registries. Fellows will have the opportunity to lead several important research studies. The ideal candidate would be a team player and have a doctoral degree in pharmacoepidemiology and ideally a clinical background, or a degree in medicine combined with pharmacoepidemiology/ epidemiology training.
- Generating rigorous high-quality real-world evidence on the comparative effectiveness and safety of treatments in rheumatologic, immunologic, and musculoskeletal conditions: Division members are actively working on methodological and substantive studies to provide rigorous real-world data on treatment patterns and comparative effectiveness and safety of various drugs for rheumatoid arthritis, psoriatic arthritis, osteoarthritis, gout, inflammatory bowel disease, and osteoporosis. The ideal candidate would have a doctoral degree in pharmacoepidemiology and ideally a clinical background, or a degree in pharmacy or medicine combined with pharmacoepidemiology/ epidemiology training.
- Developing cutting edge tools that improve causal inference by incorporating machine learning and deep learning methods using electronic health records (EHRs) and claims data: A fellow working in this area will lead a series of studies aimed at expanding the capacity of machine learning methods to make causal inference in comparative effectiveness research in a semi-automated and data-adaptive fashion. Division faculty have access to multiple large-scale datasets that link longitudinal claims data with EHR data, including both structured data and free-text clinical notes and reports. Opportunities for both methodological and applied epidemiological research are available. Specific topic areas include, but are not limited to: data-adaptive high-dimensional causal inference analytics applying machine learning and deep learning methods to claims and EHR data; and natural language processing of unstructured data for confounding adjustment, risk profiling, and patient phenotyping.
- Advancing and applying innovative methods for studying outcomes of drug-drug interactions in electronic healthcare data: Division members are actively working on methodological and substantive studies to advance evidence generation related to clinical outcomes of drug-drug interactions. Methodological work includes both innovative large-scale screening approaches and improvements in study design as applied to drug-drug interactions. Current applied work focuses on drug interactions with opioids as well as other substantive clinical areas, such as diabetes. The ideal candidate would have a doctoral degree in pharmacoepidemiology and ideally a clinical background, or a degree in pharmacy or medicine combined with pharmacoepidemiology/epidemiology training.