HAR6178: Further Statistical Methods for Health Economic Analysis

The Further Statistical Methods for Health Economic Analysis module runs in the Spring semester and is worth 15 credits.


The Further Statistical Methods for Health Economic Analysis module runs in the Spring semester and is worth 15 credits. The course leader is Jean Hamilton.

It is one of the modules on:

This module is available as a CPD option

This module is available University-wide in any year as a DDP module


The module builds on key concepts introduced in the Medical Statistics and Evidence Synthesis (HAR6170) and Economic Evaluation (HAR6260) modules in order to develop further the statistical skills needed in health economics.

Students are introduced to a range of advanced statistical topics to address issues that arise in cost effectiveness analyses, including the analysis of cost data, time-to-event data, and treatment effect estimation using data from observational studies.


This module aims to:

  • Provide students with an overview of advanced statistical methods that are necessary in health economics.
  • Provide insight into how the results of statistical analyses are used to inform economic models.

Learning outcomes

By the end of the module, a student will be able to:

  1. Select and apply appropriate regression based models to cost data.
  2. Describe how observational data can be used to explain causal inference and be able to select and apply the appropriate methodology to estimate treatment effects.
  3. Explain when and why alternative approaches to survival modelling may be used and assess their suitability in practice.
  4. Describe the issues in the development and interpretation of three-state survival models.
  5. Explain how different treatment switching adjustment methods work, and why and when it is necessary to adjust for treatment switching.
  6. Critically evaluate statistical analysis carried out for inclusion in economic evaluation models.
  7. Conduct analyses for data on costs, time to event, and observational data using specialist statistical software.

Teaching methods

Classes will be a mix of traditional lectures, seminars and computer based problem solving classes (29 hours).

The primary concepts will be introduced by the lecturer. Students will develop the ability to apply these concepts during the computer based problem solving exercises. These will provide experience of conducting analyses using standard software.

The combination of concepts introduced using a lecture style approach, class-based discussion, and independent study will enable students to critically evaluate relevant evidence.


Students will also be encouraged to engage in independent study through activities such as reading articles in peer reviewed journals (approximately 120 hours total, including preparation for the examination).

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