HLSA776
Advanced Empirical Methods in Health Services Research
Students will be introduced to advanced empirical methods to analyze unique data features in health services research (HSR). It provides the foundation for applying commonly used econometric models to conduct health services and policy research using secondary datasets. The course may cover the following topics: Generalized Linear Model, Two-Part Model, Count Models, Fixed Effects, Random effects, Multilevel/hierarchical Models, Generalized Estimating Equations (GEE), Survey data analysis, and machine learning algorithms with HSR applications.
During the Spring 2020 and Spring 2021 semesters, students could choose to take some of their courses pass-fail mid-semester which skews grade data aggregated across multiple semesters.
Average GPA of 3.93 between 14 students*
* "W"s are considered to be 0.0 quality points. "Other" grades are not factored into GPA calculation. Grade data not guaranteed to be correct.