Skip to main content

Unit information: Advanced Epidemiology in 2020/21

Unit name Advanced Epidemiology
Unit code BRMSM0015
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Savovic
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:

  • Explain how confounding, selection bias, and information bias can arise within different epidemiological studies, and how they can affect findings
  • Use causal diagrams (directed acyclic graphs: DAGs) to summarise assumptions about causal relationships, identify sources of bias, and select which variables to adjust for
  • Apply statistical methods to address confounding
  • Describe the application of causal inference methods to the analysis of randomized trials
  • Describe epidemiological studies including cohort studies, case-control studies, and more advanced approaches (e.g. instrumental variable, regression discontinuity designs, negative control, matched designs) and interpret the application of these designs to studies based on routine data and electronic health records
  • Describe statistical methods designed to strengthen causal inferences in epidemiology including propensity scores, inverse probability weights and instrumental variable methods
  • Use sensitivity analyses and negative control approaches to assess whether results are robust to the presence of different biases
  • Explain the concept of triangulation.

Intended learning outcomes

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

  1. Draw and interpret causal diagrams (directed acyclic graphs: DAGs) to summarise assumptions about causal relationships, identify sources of bias and inform model selection
  2. Describe challenges in observational epidemiology that limit its ability to establish causal effects
  3. Interpret results of statistical analyses in the light of potential confounding, selection, and information biases
  4. Explain the causal effects that can be estimated from randomized trials, and use appropriate methods for their estimation
  5. Critically appraise published epidemiological studies with regards to confounding, selection and information biases
  6. Describe key features of the important epidemiological study designs, and explain their strengths, limitations and role in modern epidemiology

Teaching details

Teaching will include learning activities set by the tutor including lectures (synchronous and asynchronous), small group work, discussions, individual tasks, and practical activities (face to face or online).

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

Assessment Details

Formative assessment will support learning by using informal questioning, homework, quizzes and group exercises in lectures and tutorials. These will form assessments for learning and will not contribute to the final unit mark. Feedback will be provided in the form of model answers and through group discussion.

Summative assessments: This unit is assessed by two pieces of coursework:

  • Group exercise to develop and justify a DAG for a study addressing a proposed research question. Students will prepare a brief presentation with their causal diagram and an explanation of the reasoning behind it. Students will be provided with a model answer to the exercise and will be asked to carry out peer-marking in groups (20% of total mark; ILO 1).
  • Individual assignment consisting of short answer style questions based on problem solving or interpretation of hypothetical scenarios and/or real data from published studies and/or elements of critical appraisal (80% of total mark; ILOs 1-6)

An average mark of 50% across the two assessments is required to pass the unit.

Reading and References

There is no essential unit text book.

Recommended reading:

  1. Rothman, Greenland, Lash. Modern Epidemiology. Third edition.
  2. Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC. (