RHSCI 7300 - Quantitative Methods for Health Care Economics

This course is designed for graduate students with a prior background in health economics and statistics at the level of Multivariate Statistics (or its equivalent). This means that students should have considerable experience with ordinary-least-squares (OLS) regression: I assume you have an understanding of multiple OLS/Logistic regressions and an ability to conduct such analyses using some statistical software (e.g. Stata, R, SAS, etc.). The major topics of the course will include claims data analysis, applied statistical approaches, and big data analytics. The goals of the course are to provide students with an overview of advanced statistical methods that are necessary for health care economics. Further, this course will provide insight into how the results of statistical analyses are used to inform economic models and develop the skills necessary to identify an appropriate technique, estimate models, and interpret results for independent research, and critically evaluate health care including health economics and health services research using advanced quantitative methods. This course will provide you with a solid foundation in the structure of claims data, cross-sectional and longitudinal data analysis, which is a type of advanced quantitative skill that is in high demand in many fields, both in and out of academia. We will be using Stata and R for all the analyses throughout the course. Students are expected to have had some exposure to the use of Stata and R. As the focus of the class is on the practical application of methods for diverse data analyses, problems will involve using statistical software to carry out analyses on real data sets.