Dr. Dickerman’s research is broadly focused on using causal inference methodology and large health care databases to improve and personalize cancer control.There is growing interest in precision prevention and early detection strategies for cancer, which offers the promise of personalized strategies tailored to an individual’s evolving characteristics. However, identifying the most effective and safest dynamic (personalized) strategies is challenging because a randomized trial may be infeasible. The intersection of large databases of electronic health records (EHRs) and causal inference methodology provides the opportunity to inform decision-making about dynamic strategies as well as the design of more efficient trials.Dr. Dickerman and her collaborators are developing a framework to evaluate dynamic cancer control strategies at scale using nationwide databases of EHRs; extending this framework to different cancer control questions, study designs, and data sources; and exploring how this may enable a learning health system that quickly translates results from big data analyses to patient populations.Dr. Dickerman serves as Co-Director of the VA-CAUSAL Methods Core, an initiative of the U.S. Veterans Health Administration to integrate high-quality data and explicitly causal methodologies to help transform the VA into a nationwide learning health system. She teaches causal inference methodology at the Harvard T.H. Chan School of Public Health.
