Handbook

This course is offered in two modes: either face to face (on-campus) and fully online.

This course provides a sound grounding in the theory and practice of fitting statistical regression models, with particular focus on the flexibility of generalised linear models (GLMs). Starting with linear regression, a major theme of the course is best practice in model fitting, including thorough exploratory data analysis, model assumption checking, data preparation and transformation, including the use of imputation, and careful attention to model adequacy and diagnostics. Emphasis is given to content-aware, purposive model building and the use of Directed Acyclic Graphs (DAGs) of causal relations to inform model parameter selection. Non-linear, logistic, binomial and Poisson models for count data are also covered. Effect modifications (interactions) and their meaning in a health context are explored. The presentation and visualisation of statistical models is considered, with emphasis on the explanatory insights that can be gained from well-constructed models. The final part of the course covers basic time-series models, survival analysis and other time-to-event models.