Overview
Advanced Biostatistics develops the statistical reasoning and applied modelling skills required for biomedical, public health, epidemiological, and clinical research. Topics include study design, experimental and observational data, confounding, effect modification, causal reasoning, missing data, repeated and clustered measurements, generalized linear models, generalized estimating equations, mixed-effects models, and longitudinal analysis.
The course also examines survival analysis, censoring, Kaplan–Meier estimation, Cox proportional hazards models, competing risks, clinical trials, diagnostic testing, epidemiologic measures, and introductory causal inference using propensity-score methods. Additional applications include logistic and Poisson regression, time-dependent covariates, multiple comparisons, and sample-size planning.
Students learn to align study designs and statistical models with scientific questions, verify assumptions, interpret adjusted effects and uncertainty, address bias and missingness, and communicate findings transparently. Emphasis is placed on responsible handling of sensitive health data, reproducible analysis, critical appraisal of published research, and reporting standards for clinical and public-health studies.
Learning Outcomes
- Evaluate biomedical, epidemiological, and clinical study designs in relation to research questions, estimands, sources of bias, and ethical data requirements.
- Select and justify generalized linear, generalized estimating equation, mixed-effects, longitudinal, and survival models for structured health data.
- Diagnose confounding, effect modification, missing-data mechanisms, model misspecification, and violations of statistical assumptions.
- Estimate and interpret adjusted effects, uncertainty intervals, survival probabilities, hazard ratios, competing-risk measures, and diagnostic-test parameters.
- Construct power and sample-size calculations for selected clinical, epidemiological, and biomedical study designs.
- Apply introductory causal-inference and propensity-score methods while identifying their assumptions and limitations.
- Synthesize results from reproducible statistical workflows and present conclusions accurately to clinicians, researchers, and public-health stakeholders.
- Critique published statistical analyses against relevant research, clinical-trial, and observational-study reporting standards.,”],
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 | Critical appraisal of a published biomedical or epidemiological study | 15.00% |
| Deliverable | Statistical analysis report using a reproducible workflow | 25.00% |
| Test | Applied modelling practical test | 20.00% |
| Exam | Final examination | 40.00% |
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.