Course overview
This course focuses on conceptual and practical issues in the design, interpretation and appraisal of epidemiological research. There is a particular emphasis on improving causal inference through design and analysis. The course content builds on the introductory courses in both epidemiology and biostatistics. Practical and tutorial sessions will provide hands-on experience with the concepts and techniques discussed in lectures.
Course learning outcomes
- Understand differences between descriptive, predictive and causal epidemiology
- Be able to pose a sufficiently well-defined descriptive, predictive or causal question
- Understand different types of causal thinking in epidemiology and application of “triangulation” of epidemiological evidence to assess causation
- Understand the potential outcomes approach (POA) to causation
- Capture a theoretical causal model using Directed Acyclic Graphs (DAG)
- Understand the role and basic principles of high quality predictive epidemiology
- Appreciate the importance and basic principles of high quality descriptive epidemiology
- Understand and identify potential sources of systematic error (bias) arising from confounding, selection and information biases
- Understand potential methods to reduce confounding, selection and information bias
- Understand random error and apply correct interpretations of P values, and confidence intervals as measures of “uncertainty”
- Understand different types of effect estimates from Randomised Control Trials (RCTs) and Quasi-experimental study designs
- Understand “Target Trial Emulation” in using observational data to estimate causal effects
Degree list
The following degrees include this course