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Published on: September 19, 2012
Characterizing structural uncertainty in decision analytic models: a review and application of methods
Laura Bojke1, Karl Claxton, Mark Sculpher
1Centre for Health Economics, University of York,York, UK. lg116@york.ac.uk
Summary
Characterizing structural uncertainties in cost-effectiveness analysis is crucial for decision-making. Explicitly modeling these uncertainties, such as clinical uncertainty, informs the need for further research and evidence generation.
Area of Science:
- Health economics
- Decision analysis
- Mathematical modeling
Background:
- Cost-effectiveness analysis (CEA) critically requires uncertainty characterization, especially for decisions on additional evidence.
- Beyond parameter and methodological uncertainty, structural uncertainties arise from model simplifications and scientific judgments.
Purpose of the Study:
- To identify and characterize sources of structural uncertainty in decision analytic models.
- To explore methods for explicitly characterizing these uncertainties within models.
Main Methods:
- Conducted separate reviews to identify types of structural uncertainty and methods for their characterization.
- Applied identified methods (model selection, model averaging, parameterization) to four distinct decision models.
Main Results:
- Identified four themes of structural uncertainty: comparator inclusion, event inclusion, statistical estimation methods, and clinical uncertainty.
- Demonstrated that cost-effectiveness and the value of research can be sensitive to structural uncertainties and their characterization methods.
Conclusions:
- Explicitly incorporating structural uncertainties into models is vital for informed decision-making.
- Parameterizing uncertainty directly within the model is essential to guide decisions on further research.
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