, Volume 29, Issue 1, pp 35–49 | Cite as

Calibrating Models in Economic Evaluation

A Seven-Step Approach
  • Tazio Vanni
  • Jonathan Karnon
  • Jason Madan
  • Richard G. White
  • W. John Edmunds
  • Anna M. Foss
  • Rosa Legood
Review Article Calibrating Models in Economic Evaluation


In economic evaluation, mathematical models have a central role as a way of integrating all the relevant information about a disease and health interventions, in order to estimate costs and consequences over an extended time horizon. Models are based on scientific knowledge of disease (which is likely to change over time), simplifying assumptions and input parameters with different levels of uncertainty; therefore, it is sensible to explore the consistency of model predictions with observational data. Calibration is a useful tool for estimating uncertain parameters, as well as more accurately defining model uncertainty (particularly with respect to the representation of correlations between parameters). Calibration involves the comparison of model outputs (e.g. disease prevalence rates) with empirical data, leading to the identification of model parameter values that achieve a good fit.

This article provides guidance on the theoretical underpinnings of different calibration methods. The calibration process is divided into seven steps and different potential methods at each step are discussed, focusing on the particular features of disease models in economic evaluation. The seven steps are (i) Which parameters should be varied in the calibration process? (ii) Which calibration targets should be used? (iii) What measure of goodness of fit should be used? (iv) What parameter search strategy should be used? (v) What determines acceptable goodness-of-fit parameter sets (convergence criteria)? (vi) What determines the termination of the calibration process (stopping rule)? (vii) How should the model calibration results and economic parameters be integrated?

The lack of standards in calibrating disease models in economic evaluation can undermine the credibility of calibration methods. In order to avoid the scepticism regarding calibration, we ought to unify the way we approach the problems and report the methods used, and continue to investigate different methods.


Economic Evaluation Markov Chain Monte Carlo Calibration Process Calibration Target Global Extremum 
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.



No sources of funding were used to conduct this study or prepare this manuscript. The authors have no conflicts of interest that are directly relevant to the content of this review.

For the invaluable advice provided, the authors thank Michael Pickles and Andrew Cox.

Supplementary material

40273_2012_29010035_MOESM1_ESM.xls (2.7 mb)
Supplementary material, approximately 2804 KB.


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Copyright information

© Springer International Publishing AG 2011

Authors and Affiliations

  • Tazio Vanni
    • 1
    • 2
  • Jonathan Karnon
    • 3
  • Jason Madan
    • 4
  • Richard G. White
    • 2
  • W. John Edmunds
    • 2
  • Anna M. Foss
    • 2
  • Rosa Legood
    • 1
  1. 1.Health Services Research Unit, Department of Public Health and PolicyLondon School of Hygiene and Tropical MedicineLondonUK
  2. 2.Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical MedicineLondonUK
  3. 3.School of Population Health and Clinical Practice, University of AdelaideAdelaideAustralia
  4. 4.Academic Unit of Primary Health Care, University of BristolBristolUK

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