The purpose of this course run by the Centre for Epidemiology and Biostatistics, in association with The University of Adelaide, is to provide a systematic overview of epidemiological concepts and methods, building up from sources of error (confounding, information bias, selection bias) to bias analysis methods, then a range of contemporary methods. The course is premised on a counterfactual and potential outcomes approach to epidemiology.
This course has been taught in Australia by Professor Tony Blakely and John Lynch for many years, and is highly regarded.
The next course will be held on the 24th to 27th September 2018 at the Graduate House, The University of Melbourne.
What does the course include?
- An introduction to causal inference using contemporary approaches such as a potential approach model and directed acyclic graphs (DAGs).
- A comprehensive overview of systematic error (confounding, selection and information biases).
- An introduction to quantitative bias analysis methods to correct for systematic error in epidemiological studies. (Sometimes called sensitivity analyses.) Methods taught range from simple to probabilistic methods.
- Quantitative bias analysis exercises using Excel spreadsheets. Understanding and applying bias analyses not only enables you to undertake your own analyses in the future, but also means you have a deeper understanding of systematic error.
- Selected specific topics such as regression model building strategies, effect measure modification and interaction; direct and indirect effects (i.e. mediation analysis), propensity scores, instrument variables; null hypothesis significance testing and p values.
- Day 4 focuses on applications of some of these methods in disease and cost effectiveness simulations (e.g. Markov and multistate lifetable models), and applications of G methods such as Marginal Structural Models (MSMs) and causal mediation analysis to complex longitudinal data with time-varying confounding and mediation.
The course is recommended as a four day package. However, Day 4 focuses on addressing policy questions using advanced epidemiological methods, e.g. disease and cost-effectiveness modelling, and analysis of complex longitudinal data. Some participants already conversant with advanced epidemiological methods (e.g. have attended this course before) may enrol for just Day 4.
Who is the course designed for?
The course is designed for 2 target audiences:
- PhD students, early career researchers, and advanced MPH students who wish to extend their knowledge to include some of the more advanced epidemiological ways of thinking and methods that have merged over the last decade.
- More senior investigators who want an efficient “catch up” on some of the new thinking and methods being used in higher quality research publications
This course will assume knowledge of study design and analytical methods, the basic principles of systematic error (confounding, selection and information biases) and biostatistics up to multivariable regression. For example, successful completion of a Diploma or Masters of Public Health course in epidemiology and biostatistics (or similar) will usually provide the necessary basis to undertake this course. If you wish to discuss your suitability for this course, please contact: email@example.com