This is a review of calibration and validation methods in mathematical modeling. Such models that attempt to capture structural relationships between their components and combine information from multiple sources are increasingly used in medicine. The authors provide an overview of methods for model validation and calibration and survey studies comparing alternative approaches. Model validation entails a confrontation of models with data, background knowledge, and other models, and can inform judgments about model credibility.
Calibration involves selecting parameter values to improve the agreement of model outputs with data. When the goal of modeling is quantitative inference on the effects of interventions or forecasting, calibration can be viewed as estimation. This view clarifies issues related to parameter identifiability and facilitates formal model validation and the examination of consistency among different sources of information. In contrast, when the goal of modeling is the generation of qualitative insights about the modeled phenomenon, calibration is a rather informal process for selecting inputs that result in model behavior that roughly reproduces select aspects of the modeled phenomenon and cannot be equated to an estimation procedure.
Current empirical research on validation and calibration methods consists primarily of methodological appraisals or case-studies of alternative techniques and cannot address the numerous complex and multifaceted methodological decisions that modelers must make. Further research is needed on different approaches for developing and validating complex models that combine evidence from multiple sources.
Dahabreh IJ, Wong JB, Trikalinos TA. Validation and Calibration of Structural Models that Combine Information from Multiple Sources. Expert Review of Pharmacoeconomics and Outcomes Research 2017; 17 (1): 27-37. https://doi.org/10.1080/14737167.2017.1277143
Not open access.