, Volume 27, Issue 7, pp 533–545 | Cite as

Calibration Methods Used in Cancer Simulation Models and Suggested Reporting Guidelines

  • Natasha K. StoutEmail author
  • Amy B. Knudsen
  • Chung Yin Kong
  • Pamela M. McMahon
  • G. Scott Gazelle
Review Article


Increasingly, computer simulation models are used for economic and policy evaluation in cancer prevention and control. A model’s predictions of key outcomes, such as screening effectiveness, depend on the values of unobservable natural history parameters. Calibration is the process of determining the values of unobservable parameters by constraining model output to replicate observed data. Because there are many approaches for model calibration and little consensus on best practices, we surveyed the literature to catalogue the use and reporting of these methods in cancer simulation models.

We conducted a MEDLINE search (1980 through 2006) for articles on cancer-screening models and supplemented search results with articles from our personal reference databases. For each article, two authors independently abstracted pre-determined items using a standard form. Data items included cancer site, model type, methods used for determination of unobservable parameter values and description of any calibration protocol. All authors reached consensus on items of disagreement. Reviews and non-cancer models were excluded. Articles describing analytical models, which estimate parameters with statistical approaches (e.g. maximum likelihood) were catalogued separately.Models that included unobservable parameters were analysed and classified by whether calibration methods were reported and if so, the methods used.

The review process yielded 154 articles that met our inclusion criteria and, of these, we concluded that 131 may have used calibration methods to determine model parameters. Although the term ‘calibration’ was not always used, descriptions of calibration or ‘model fitting’ were found in 50% (n = 66) of the articles, with an additional 16% (n = 21) providing a reference to methods. Calibration target data were identified in nearly all of these articles. Other methodological details, such as the goodness-of-fit metric, were discussed in 54% (n = 47 of 87) of the articles reporting calibration methods, while few details were provided on the algorithms used to search the parameter space.

Our review shows that the use of cancer simulation modelling is increasing, although thorough descriptions of calibration procedures are rare in the published literature for these models. Calibration is a key component of model development and is central to the validity and credibility of subsequent analyses and inferences drawn from model predictions. To aid peer-review and facilitate discussion of modelling methods, we propose a standardized Calibration Reporting Checklist for model documentation.


Calibration Method Comparative Effectiveness Research Calibration Target Calibration Protocol Unobservable Parameter 
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.



The authors gratefully acknowledge the support of Drs Eric Feuer and Karen Kuntz and members of the NCI Cancer Intervention and Surveillance Modeling Network. This work was supported in part by grants from the National Cancer Institute: F32 CA125984 (Natasha K. Stout), R25 CA92203 (Amy B. Knudsen), K99 126147 (Pamela M. McMahon, Chung Yin Kong) and R01 97337 (G. Scott Gazelle, Pamela M. McMahon, Chung Yin Kong). The funding agreements ensured the authors’ independence in designing the study, collecting, analysing and interpreting the data and writing and publishing the report. An earlier version of this work was presented at the 2007 Society for Medical Decision Making Annual Meeting.

Supplementary material

40273_2012_27070533_MOESM1_ESM.pdf (129 kb)
Supplementary material, approximately 132 KB.


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Copyright information

© Adis Data Information BV 2009

Authors and Affiliations

  • Natasha K. Stout
    • 1
    • 2
    Email author
  • Amy B. Knudsen
    • 3
    • 4
  • Chung Yin Kong
    • 3
    • 4
  • Pamela M. McMahon
    • 3
    • 4
  • G. Scott Gazelle
    • 2
    • 3
    • 4
  1. 1.Department of Ambulatory Care and PreventionHarvard Medical School/Harvard Pilgrim Health CareBostonUSA
  2. 2.Program in Health Decision Science, Department of Health Policy and ManagementHarvard School of Public HealthBostonUSA
  3. 3.Institute for Technology AssessmentMassachusetts General HospitalBostonUSA
  4. 4.Department of RadiologyMassachusetts General HospitalBostonUSA

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