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Unit information: Statistics for Epidemiology in 2022/23

Please note: It is possible that the information shown for future academic years may change due to developments in the relevant academic field. Optional unit availability varies depending on both staffing, student choice and timetabling constraints.

Unit name Statistics for Epidemiology
Unit code BRMSM0032
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Jon Heron
Open unit status Not open

BRMSM0001: Introduction to Epidemiology and Statistics



School/department Bristol Medical School
Faculty Faculty of Health Sciences


The aims of this unit are to:

  • Use statistical software to manage and manipulate data
  • Conduct statistical analyses of epidemiological data, and interpret their results
  • Describe statistical methods commonly applied in epidemiology including linear, logistic, Poisson and Cox regression, their extensions to clustered data and random effects, and other methods for survival analysis
  • Use regression models to adjust for confounding, test for effect modification and model linear and nonlinear relationships
  • Construct, validate and interpret prediction models to address diagnostic and prognostic research questions
  • Understand the implications of missing data, and introduce methods to address these

Intended learning outcomes

By the end of this unit, students should be able to:

  1. Use statistical software to manipulate, describe and analyse data
  2. Conduct analyses using appropriate regression models, considering study design, type of outcome variable and potential confounders
  3. Interpret the results from regression models of epidemiological data, considering study design issues
  4. Use regression models to adjust for confounding, test for effect modification and model linear and nonlinear relationships
  5. Conduct statistical analyses for time-to-event outcomes
  6. Construct, validate and interpret prediction models for diagnosis and prognosis

Teaching details

Teaching will include learning activities set by the tutor including lectures, small group work, discussions, individual tasks, and practical activities.

Directed and self-directed learning will include activities such as reading, accessing web-based supplementary materials, critical analysis and completion of assessments

Assessment Details

Summative assessment: The unit will be assessed using a single piece of coursework:

  • Data analysis and interpretation exercise. Students will be given a data set and a set of analytical tasks to complete. They will also be asked to state the strengths and limitations of their analysis and discuss possible alternatives, with suitable justification (ILO 1-6; 100% of total unit mark).

Formative assessment:

Formative assessments will come in many forms such informal questioning, quizzes and group exercises in lectures, tutorials and homework. These form an assessment for learning and will not contribute to the final unit mark. Stata-based elements will be a feature of the majority of the practical sessions, including the one-day prediction “workshop” where students will work (and compete) in teams in an attempt to devise the best prediction model.


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