, Volume 26, Issue 9, pp 799–806 | Cite as

Subgroups and Heterogeneity in Cost-Effectiveness Analysis

  • Mark SculpherEmail author
Briefing Paper


The National Institute for Health and Clinical Excellence (NICE) is required to consider cost effectiveness when issuing guidance about the use of health technologies within the UK NHS. Cost effectiveness is a means of supporting a system objective of maximizing population health gain from the available budget.

There is a range of sources of variation between individuals in disease prognosis, and in the costs and effects of health technologies. It is often possible to explain some of this variation on the basis of the clinical and sociodemographic characteristics of patients. This facilitates subgroup-specific estimates of parameters in decision analytic models and provides a means of assessing heterogeneity in cost effectiveness between different types of patient. Given the objective of the NHS, there is a clear need for NICE, and similar decision makers in other systems, to reflect this heterogeneity by being as specific as possible about the characteristics of the recipients of new treatments.

The use of subgroup analysis in cost-effectiveness analysis raises a number of methodological questions that have been given little consideration in the literature. They include a need to define the possible sources of heterogeneity that exist, which extends beyond relative treatment effect (which is the focus of clinical trial analysis) to include, for example, sources relating to baseline event rates. There is also the issue of how heterogeneity in model parameters should be estimated and how uncertainty should be appropriately quantified. A major issue also exists concerning the appropriateness, in terms of equity, of using all or some of the subgroup analyses as a basis of decision making. NICE needed to consider these and other issues when updating its methods guidance.


Cost Effectiveness Alteplase Health Gain Relative Treatment Effect Public Preference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This paper was initially prepared as a briefing paper for NICE as part of the process of updating the Institute’s 2004 Guide to the Methods of Technology Appraisal. The work was funded by NICE through its Decision Support Unit (DSU), which is based at the universities of Sheffield, Leicester, York, Leeds and at the London School of Hygiene and Tropical Medicine.

The author has no conflicts of interest that are directly relevant to the content of this article.

The author gratefully acknowledges Karl Claxton, John Brazier and Alex Sutton for comments on earlier drafts. Any remaining errors are the responsibility of the author alone.


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Copyright information

© Adis Data Information BV 2008

Authors and Affiliations

  1. 1.Centre for Health Economics and NICE Decision Support UnitUniversity of YorkHeslington, YorkUK

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