Department of Biostatistics Seminar/Workshop Series

Data, Statistics, and Inference

Sharon-Lise T. Normand, PhD, Department of Health Care Policy, Harvard Medical School, Department of Biostatistics, Harvard T.H. Chan School of Public Health

Increased access to electronic health information have intensified attempts to understand the effect caused by new medical interventions in usual care populations. Moreover, state and global connectedness of health information has informed state-specific or country-specific public health policy decisions. By conditioning on rich confounding information, utilizing larger populations, and multiple sources of information, researchers aim to comply with key principles underpinning causal inference. During this talk, an examination of current (and future) substantive and methodological problems will be discussed. We consider Bayesian techniques for causal effect estimation using high-dimensional data, including regularizing priors and Bayesian additive regression trees. Assessing the comparative effectiveness of drug eluting or bare metal coronary artery stents illustrate methodology. Funded by R01- GM111339 and U01-FDA004493. Joint work with Jacob Spertus and Sherri Rose at Harvard Medical School.