Overview
This advanced course examines epidemiologic theory, study design, statistical modelling, and inference for complex population health questions. Students compare cohort, case-control, cross-sectional, ecological, randomized, and quasi-experimental designs, with emphasis on estimands, validity, bias, confounding, selection, effect modification, and the relationship between design choices and causal interpretation.
The course develops advanced analytical competence across binary, continuous, count, survival, repeated-measures, clustered, multilevel, longitudinal, competing-risk, and recurrent-event data. Topics include model specification and diagnostics, missing-data mechanisms and multiple imputation, misclassification, quantitative bias analysis, sensitivity analysis, standardization, and transparent reporting. Practical sessions require protocol development, data management, reproducible statistical workflows, peer review, critical appraisal, and communication of results using appropriate statistical software.
Learning Outcomes
- Evaluate epidemiologic study designs and select appropriate designs for substantive population health questions.
- Formulate estimands and analytic plans that align research questions, study designs, data structures, and inferential objectives.
- Construct and justify multivariable models for binary, continuous, count, survival, repeated-measures, clustered, and longitudinal outcomes.
- Diagnose model assumptions, assess model fit, and determine the implications of misspecification for epidemiologic inference.
- Analyze interaction, effect modification, confounding, overadjustment, selection bias, and measurement error using appropriate epidemiologic methods.
- Apply methods for competing risks, recurrent events, missing data, multiple imputation, misclassification, quantitative bias analysis, and sensitivity analysis.
- Reproduce advanced epidemiologic analyses using statistical software, documented data-management procedures, and version-controlled workflows.
- Interpret adjusted, standardized, and model-based estimates in relation to internal validity, external validity, and causal claims.
- Critique advanced epidemiologic literature using established reporting standards and principles of transparent research.
- Synthesize and communicate complex epidemiologic findings accurately to technical and non-technical audiences.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 2 hours | Weekly | All semester |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Fortnightly | All semester |
| Workshop | 2 hours | Fortnightly | All semester |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Protocol and analytic plan | 15.00% |
| Deliverable | Data management and reproducible workflow | 15.00% |
| Test | Practical modelling tests (2 × 10%) | 20.00% |
| Assignment | Critical appraisal and peer review | 10.00% |
| Capstone | Applied epidemiologic analysis project | 20.00% |
| Exam | Final examination | 20.00% |
Prerequisites
Teaching Staff & Programs
This course is delivered jointly by faculty from the participating programs listed below. In line with the Douchewater Way, the University of Sexology tailors core instruction directly to each cohort's specific discipline — adapting curriculum to program needs rather than forcing students into a one-size-fits-all model. Learn more about our approach at The Douchewater Way.
