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The calibration of cancer natural history models is often challenged by a lack of representative calibration targets, forcing modellers to rely on potentially incompatible datasets. Using a microsimulation colorectal cancer model as an example, the purposes of this paper are to (1) highlight the reasons for uncertainty in calibration targets, (2) illustrate practical and generalisable approaches for dealing with incompatibility in calibration targets, and (3) discuss the importance of future research in the area of incorporating uncertainty in calibration. The low quality of data and differences in populations, outcome definitions, and healthcare systems may result in incompatibility between the model and the data. Acknowledging reasons for data incompatibility allows assessment of the risk of incompatibility before calibrating the model. Only a few approaches are available to address data incompatibility, for instance addressing biases in calibration targets and their adjustment, relaxing the goodness-of-fit metric, and validation of the calibration targets to the data not used in the calibration. However, these approaches lack explicit comparison and validation, and so more research is needed to describe the nature and causes of indirect uncertainty (i.e. uncertainty that cannot be expressed in absolute quantitative forms) and identify methods for managing this uncertainty in healthcare modelling. |
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