, Volume 35, Issue 8, pp 767–776 | Cite as

How to Appropriately Extrapolate Costs and Utilities in Cost-Effectiveness Analysis

  • Laura BojkeEmail author
  • Andrea Manca
  • Miqdad Asaria
  • Ronan Mahon
  • Shijie Ren
  • Stephen Palmer
Practical Application


Costs and utilities are key inputs into any cost-effectiveness analysis. Their estimates are typically derived from individual patient-level data collected as part of clinical studies the follow-up duration of which is often too short to allow a robust quantification of the likely costs and benefits a technology will yield over the patient’s entire lifetime. In the absence of long-term data, some form of temporal extrapolation—to project short-term evidence over a longer time horizon—is required. Temporal extrapolation inevitably involves assumptions regarding the behaviour of the quantities of interest beyond the time horizon supported by the clinical evidence. Unfortunately, the implications for decisions made on the basis of evidence derived following this practice and the degree of uncertainty surrounding the validity of any assumptions made are often not fully appreciated. The issue is compounded by the absence of methodological guidance concerning the extrapolation of non-time-to-event outcomes such as costs and utilities. This paper considers current approaches to predict long-term costs and utilities, highlights some of the challenges with the existing methods, and provides recommendations for future applications. It finds that, typically, economic evaluation models employ a simplistic approach to temporal extrapolation of costs and utilities. For instance, their parameters (e.g. mean) are typically assumed to be homogeneous with respect to both time and patients’ characteristics. Furthermore, costs and utilities have often been modelled to follow the dynamics of the associated time-to-event outcomes. However, cost and utility estimates may be more nuanced, and it is important to ensure extrapolation is carried out appropriately for these parameters.


Individual Patient Data Health Assessment Questionnaire Score Psoriasis Area Severity Index Decision Uncertainty Baseline Utility 
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.



Laura Bojke and Andrea Manca were primarily responsible for drafting the manuscript. Stephen Palmer, Miqdad Asaria, Ronan Mahon and Shijie Ren contributed towards writing and commented on various versions of the manuscript.

Compliance with Ethical Standards


Work contributing to this manuscript was conducted as part of a Medical Research Council (MRC) grant “Methods of extrapolating RCT evidence for economic evaluation”, although this manuscript was not prepared during the time of this grant. Laura Bojke was supported in the preparation/submission of this paper by the Health Economics and Outcome Measurement (HEOM) Theme of the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care Yorkshire and Humber (NIHR CLAHRC YH; The views and opinions expressed are those of the authors, and not necessarily those of the UK National Health Service (NHS), the NIHR or the Department of Health.

Conflicts of interest

Laura Bojke, Andrea Manca, Miqdad Asaria, Ronan Mahan, Stephen Palmer and Shijie Ren all have no conflicts of interest.


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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Centre for Health EconomicsUniversity of YorkYorkUK
  2. 2.University of SheffieldSheffieldUK

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