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Unit information: Statistics for Epidemiology in 2019/20

Please note: Due to alternative arrangements for teaching and assessment in place from 18 March 2020 to mitigate against the restrictions in place due to COVID-19, information shown for 2019/20 may not always be accurate.

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

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
Pre-requisites

Introduction to Epidemiology and Statistics

Co-requisites

None

School/department Bristol Medical School
Faculty Faculty of Health Sciences

Description

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 clustered and missing data, and introduce methods to address these - Understand simulation as a method to aid design, analysis or interpretation of a study - Introduce applications of machine learning in epidemiology

Intended learning outcomes

On successful completion of the 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
  7. Describe and use methods for dealing with clustered and missing data
  8. Simulate data to aid the design, analysis or interpretation of a study
  9. Describe uses and applications of machine learning in epidemiology

Teaching details

There will be 10 teaching weeks, plus reading week and revision week.

Face to face teaching for a total of 50 hours will include lectures and tutorials. Directed and self-directed learning (150 hours) will include activities such as reading, analysing and interpreting data, and preparation for assessment.

Assessment Details

There will be two formative assessments: the first will be a practical session on constructing and validating a risk prediction model (ILO 6). The second formative assessment will be a practical session on using simulation to inform the design of a future study (ILO 8).

Summative assessment will be in the form of a data analysis and interpretation exercise. Students will be given a data set and a set of analysis 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 1-9).

A mark of 50% for the summative coursework assessment is required to pass the unit.

Reading and References

There is no compulsory unit text book.

Recommended reading:

  1. Kirkwood BR, Sterne JAC. (2010) Essential Medical Statistics. Blackwell.
  2. Sterne, JAC et al. (2009). Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 338:157-160.
  3. Cleeves M, Gould W, Gutierrez R, Marchenko Y. An Introduction to Survival Analysis Using Stata, 3rd edition. Stata Press 2010.

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