Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location in first half year 2021.
August - Dual-Delivery
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This subject is a core subject within the Master of Epidemiology and the Master of Science (Epidemiology) and an elective within the Master of Public Health, the Master of Environment, and the Master of Biostatistics.
Epidemiology 2 is an in-depth exploration of research study design and development. Within this subject, students will learn the most contemporary approaches to designing studies for clinical and public health research and analysing their results.
Students will develop the skills to design and critique a variety of experimental and observational studies (cluster randomised controlled trials, case control study variants, ecological and multilevel studies). Complex causal diagrams to guide identifying confounders when planning the study and selection of confounders in the analysis, and assessing bias are introduced. Students learn how to apply quantitative bias analyses to quantify the direction and magnitude of bias in clinical and public health studies.
Several methods to control for confounding are introduced and students will learn to apply these methods in their future research. The limitations of regression are discussed. Students will learn the latest strategies for selecting confounders to control.
The concept of effect measure modification, how it differs from interaction, and how it impacts external validity is discussed. Students will apply this knowledge to estimate the potential effects of population and clinical interventions when implementing research findings.
Intended learning outcomes
On completion of this subject, students are expected to be able to:
- Judge the impact of effect measure modification on external validity and the implementation of population and clinical interventions
- Assess confounding and collider bias by creating causal diagrams
- Apply standardisation, regression, propensity scores, and g-computation to control for confounding
- Apply quantitative bias techniques to quantify the direction and magnitude of bias
- Design experimental and observational epidemiological studies
- Critique experimental and observational epidemiological studies
Last updated: 1 March 2021