Handbook

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

Sophisticated modelling techniques are essential for the analysis of real-world health data. Building on Health Data Analytics: Statistical Modelling I (HDAT9600), this course expands the statistical toolkit and broadens students' understanding of relevant statistical approaches for the analysis of realistically complex data structures and research questions. The course is aimed at those currently working or planning on working in health or a health-related field, and who are interested in applying advanced statistical methods to analyse complex data.

Topics covered in this course include multilevel models for hierarchical data; analysis of time series and longitudinal data; quasi-experimental approaches for drawing causal inferences from observational data; multiple imputation for missing values; and simulation approaches for study planning and model evaluation.

Content is delivered through a combination of online readings, expert guest lectures and practical hands-on tutorials. Statistical concepts are illustrated with a variety of health examples, and students will learn how to implement methods using leading statistical software. Lectures are followed by weekly exercises, which reinforce the learning and programming skills covered in the face-to-face tutorials.