Jane Kim, Stephen Resch, Nicole Campos, and Vidit Munshi took part in the Kolokotrones Symposium on Data Science entitled “Evidence-based policy: How can we use health data to better microsimulation models?”, sponsored by CHDS and the Program on Casual Inference. Fourth-year doctoral student Munshi presented “Why do we use microsimulation models?” on the limitations of data that is typically available for analysis and policy-relevant research. Campos, a Research Scientist at CHDS, spoke about an analytic approach that can be used to integrate epidemiological and clinical data into disease natural history models using the example of a natural history model of human papillomavirus (HPV) infection and cervical carcinogenesis. Professor Miguel Hernan, Professor Jane Kim, and Dr. Resch, then held a panel to field questions from the audience and discuss the presentations from Postdoctoral Fellow in Epidemiology Eleanor Murray, Campos, and Munshi.
About the Kolokotrones Symposium:
The Kolokotrones Symposium is a monthly gathering focused on discussing methodologic issues in that arise in data science, including epidemiology and biostatistics, in a more relaxed setting. These monthly meetings are organized as methods clinics during which students, postdocs, and other junior investigators present questions about methodological challenges that they encounter in their research. Possible solutions are discussed by the group, which includes faculty members from the Departments of Biostatistics and Epidemiology. The discussion topics can encompass any aspect of data science, including database management, design of observational analyses, machine learning algorithms, and causal inference techniques. The first and second symposia were very well-attended and included lively discussion that was enjoyed by all. All Kolokotrones symposia take place on the first Fridays of each month, from 3:45-5:30pm in the Ballard Room at Countway Library. If you are interested in attending, or would like to participate in future symposia, please contact Lucia Petito (firstname.lastname@example.org).