Overview
This advanced course examines the design, analysis, and interpretation of biomedical and public health studies. Students develop expertise in formulating research questions and estimands; selecting observational and experimental designs; applying sampling strategies; and addressing bias, confounding, effect modification, missing data, measurement reliability, statistical power, sample-size requirements, and research ethics.
The course covers descriptive analysis, estimation and confidence intervals, hypothesis testing, regression modeling, analysis of variance, logistic regression, survival analysis, repeated-measures methods, and introductory causal inference. Emphasis is placed on aligning methods with study objectives and data structures, assessing model assumptions, producing reproducible statistical workflows, communicating uncertainty, and distinguishing statistical association from causal inference. Students critique published research, prepare an analysis plan, conduct an end-to-end analysis using statistical software, and present findings to technical and nontechnical audiences.
Learning Outcomes
- Formulate biomedical and public health research questions, estimands, and analysis objectives appropriate to specific study designs.
- Evaluate cross-sectional, case-control, cohort, randomized controlled, and other observational and experimental designs for validity and suitability.
- Assess sampling strategies, sources of bias, confounding, effect modification, measurement reliability, missing-data mechanisms, and ethical considerations.
- Calculate and justify statistical power and sample-size requirements for common study designs and analytical objectives.
- Select and apply descriptive, inferential, regression, survival, repeated-measures, and introductory causal-inference methods appropriate to study objectives and data structures.
- Diagnose violations of statistical assumptions and evaluate the implications for estimation, inference, and interpretation.
- Construct reproducible statistical workflows that document data preparation, analysis decisions, software implementation, and quality assurance.
- Critique published biostatistical analyses and identify threats to internal validity, external validity, and causal interpretation.
- Synthesize analytical findings into a coherent report and communicate statistical uncertainty accurately to technical and nontechnical audiences.
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 | Study-design critique (1 × 10%) | 10.00% |
| Assignment | Statistical computing exercises (4 × 5%) | 20.00% |
| Exam | Mid-semester examination | 20.00% |
| Deliverable | Comprehensive analysis plan | 15.00% |
| Capstone | End-to-end biostatistical analysis and presentation | 25.00% |
| Exam | Final examination | 10.00% |
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.