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Further Health Statistics - HEA00165M

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  • Department: Health Sciences
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

Module summary

If you would like to expand your knowledge of statistics to wider and more complex research applications, then this is the module for you. You will encounter a variety of regression models based on different types of outcome, including continuous, binary and time to event data. You will explore variations in treatment effects, methods to address treatment compliance and missing data as well as how to forecast future health outcomes. The focus of this module will be on recognising and interpreting these concepts using published research papers.

Module will run

Occurrence Teaching period
A Semester 2 2023-24

Module aims

To equip students with the necessary skills and knowledge to allow interpretation and critical understanding of the analysis of data including undertaking analysis in computer software. The students will also gain the ability to read health research papers.

Module learning outcomes

By the end of the module, students will be able to:

  1. Articulate an understanding of the key principles of statistical analysis.

  2. Select and conduct the appropriate statistical analysis for a research question.

  3. Articulate knowledge of how output from statistical analysis should be presented and the appropriate format to use.

  4. Critically appraise the appropriateness and interpretation of statistical analysis in health research papers.

Module content

  • Analysis methods for continuous outcomes: simple, multiple and mixed regression models
  • Analysis methods for binary outcomes: simple and multiple regression models
  • Analysis methods for time to event outcomes: survival models
  • Exploring future outcomes: prediction modelling
  • Exploring variation in treatment effects: mediators and moderators
  • Exploring the effect of treatment compliance
  • Approaches to handling missing data
  • Statistics in practice
  • Review

Indicative assessment

Task % of module mark
Online Exam -less than 24hrs (Centrally scheduled) 100

Special assessment rules

None

Indicative reassessment

Task % of module mark
Online Exam -less than 24hrs (Centrally scheduled) 100

Module feedback

Written cohort feedback for the summative assessment is provided on the standard proforma, within the timescale specified in the programme handbook.

Indicative reading

Bland, Martin (2015). An introduction to medical statistics / Martin Bland. (Fourth edition.). Oxford: Oxford University Press.

Daniel, W. W., & Cross, Chad Lee. (2010). Biostatistics: a foundation for analysis in the health sciences / Wayne W. Daniel. (10th edition.). Chichester: John Wiley.

Molenberghs, G., & Kenward, Michael G. (2007). Missing data in clinical studies / Geert Molenberghs, Michael G. Kenward. Chichester: John Wiley.

Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: a regression-based approach / Andrew F. Hayes. (Second edition.). New York, New York; London, [England]: The Guilford Press.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.