Advanced Elements of Analytics

This subject equips students with the practical skills to apply regression methods to health data using the statistical packages R and Stata, as well as a major emphasis on the interpretation and communication of results. Topics covered include: analysis of continuous outcomes with linear regression; analysis of binary outcomes with logistic and tree-based regression methods; analysis of time-to-event outcomes with Cox and Poisson regression; fitting the aforementioned regression models in the statistical packages R and Stata; interpretation of the different measures of association estimated in each of the regression models; how to adjust for confounding and identify variables that modify measures of association using these regression methods; and purpose of regression modelling (causal vs. predictive).