Overview
This course develops statistical modelling skills for medical, epidemiological, and public health applications. Students examine the structure, quality, complexity, and ethical handling of health data from clinical trials, electronic health records, population surveys, and other observational studies.
The course covers study design, data cleaning and validation, exploratory analysis, linear and generalized linear models, logistic and Poisson regression, survival and time-to-event analysis, longitudinal and repeated-measures data, missing-data mechanisms and multiple imputation, confounding, effect modification, interaction, model selection, validation, and uncertainty assessment.
Students formulate health research questions, select and implement appropriate statistical models, assess assumptions and fit, interpret coefficients and predicted risks, distinguish association from causation, and communicate results to clinical and nontechnical audiences. Emphasis is placed on reproducible workflows, transparent reporting, ethical use of sensitive information, and the connection between statistical evidence and health decisions.
Learning Outcomes
- Formulate statistically appropriate research questions for medical, epidemiological, and public health investigations.
- Evaluate health study designs, sources of bias, data structures, and limitations affecting statistical inference.
- Implement data cleaning, validation, exploratory analysis, and reproducible workflows using statistical software.
- Select and fit linear, generalized linear, survival, longitudinal, and repeated-measures models for health data.
- Assess model assumptions, goodness of fit, predictive performance, uncertainty, and sensitivity to analytical choices.
- Interpret model coefficients, effect estimates, predicted risks, and time-to-event measures in substantive health contexts.
- Distinguish statistical association from causal interpretation by evaluating confounding, effect modification, and interaction.
- Apply appropriate methods for characterizing missing-data mechanisms and implementing multiple imputation.
- Synthesize statistical findings into transparent reports that communicate limitations and practical implications to clinical and nontechnical audiences.
- Evaluate ethical and privacy considerations in the analysis, storage, reporting, and dissemination of sensitive health information.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 2 hours | Weekly | All semester |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Fortnightly | All semester |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Health data cleaning and exploratory analysis report | 15.00% |
| Assignment | Modelling exercises and interpretation portfolio | 20.00% |
| Test | Practical statistical software test | 15.00% |
| Deliverable | Reproducible health data analysis report and presentation | 25.00% |
| Exam | Final examination | 25.00% |
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.