Advanced Methods in Health Services Research

3.0 credits

The purpose of this course is to provide students with a strong understanding of, and experience in, advanced quantitative methods for health services research. Topics covered will be longitudinal models (e.g. fixed and random effects, conditional, marginal and structural models), causal inference (e.g. difference-in-differences, propensity score methods, instrumental variables, regression discontinuity, and quantile/nonlinear regression), and practical considerations for handling data (e.g. missing data, data structures, effective programming). Time permitting, we will also cover spatial methods and some topics in Big Data. The course will be taught by lecture and hands-on sessions. The emphasis of the course will be on applications that will be useful for students to implement in their thesis work. Prerequisite: HSR doctoral student or permission of instructor