Research Abstract

Dr. Dominici’s research has focused on the development of statistical methods for the analysis of large observational data and in particular large administrative data bases such as Medicare and SEER-Medicare. She is an expert in Bayesian methods, longitudinal data analysis, confounding adjustment, causal inference, and Bayesian hierarchical models. She is the PI, together with Dr. Xihong Lin, of a NCI Program Project (P01) entitled “Statistical Informatics for Cancer Research” (http://www.hsph.harvard.edu/statinformatics/index.html). This program project will tackle a series of problems motivated by the analysis of high dimensional data arising in population-based studies of cancer.

Dr. Dominici is has become more involved in comparative effectiveness research collaborating with investigators at Dana Farber Cancer Institute (Dr.. Deborah Schrag and Dr.Nils Arvold). With her colleagues she is developing statistical methods for causal inference and propensity score matching to compare health care delivery systems in end of life cancer, with a special focus on glioblastoma and pancreatic cancer. Dr. Dominici also oversees the management and the analysis of several administrative databases, including Part A CMS files and SEER-Medicare.