title: Characterizing structural uncertainty in decision analytic models: a review and application of methods.
abstract: BACKGROUND
  The characterization of uncertainty is critical in cost-effectiveness analysis, particularly when considering whether additional evidence is needed. In addition to parameter and methodological uncertainty, there are other sources of uncertainty which include simplifications and scientific judgments that have to be made when constructing and interpreting a model of any sort. These have been classified in a number of different ways but can be referred to collectively as structural uncertainties.
  
  
  METHODS
  Separate reviews were undertaken to identify what forms these other sources of uncertainty take and what other forms of potential methods to explicitly characterize these types of uncertainties in decision analytic models. These methods were demonstrated through application to four decision models each representing one of the four types of uncertainty.
  
  
  RESULTS
  These sources of uncertainty fall into four general themes: 1) inclusion of relevant comparators; 2) inclusion of relevant events; 3) alternative statistical estimation methods; and 4) clinical uncertainty.Two methods to explicitly characterize such uncertainties were identified: model selection and model averaging. In addition, an alternative approach, adding uncertain parameters to represent the source of uncertainty was also considered.The applications demonstrate that cost-effectiveness may be sensitive to these uncertainties and the methods used to characterize them. The value of research was particularly sensitive to these uncertainties and the methods used to characterize it. It is therefore important, for decision-making purposes, to incorporate such uncertainties into the modeling process.
  
  
  CONCLUSIONS
  Only parameterizing the uncertainty directly in the model can inform the decision to conduct further research to resolve this source of uncertainty.
authors.affiliation: Centre for Health Economics, University of York,York, UK. lg116@york.ac.uk
authors.forename: Laura
authors.initials: L
authors.lastname: Bojke
authors.forename: Karl
authors.initials: K
authors.lastname: Claxton
authors.forename: Mark
authors.initials: M
authors.lastname: Sculpher
authors.forename: Stephen
authors.initials: S
authors.lastname: Palmer
mesh_terms: D054058:Acute Coronary Syndrome / Q000188:drug therapy / Q000191:economics; D001157:Arterial Occlusive Diseases / Q000191:economics / Q000628:therapy; D000077144:Clopidogrel; D003362:Cost-Benefit Analysis / Q000379:methods*; D003661:Decision Support Techniques; D006801:Humans; D008268:Macular Degeneration / Q000191:economics / Q000517:prevention & control; D008403:Mass Screening / Q000191:economics; D017059:Models, Econometric; D009062:Mouth Neoplasms / Q000191:economics / Q000517:prevention & control; D010975:Platelet Aggregation Inhibitors / Q000191:economics / Q000627:therapeutic use; D019039:Platelet Glycoprotein GPIIb-IIIa Complex / Q000037:antagonists & inhibitors; D019057:Quality-Adjusted Life Years; D013988:Ticlopidine / Q000031:analogs & derivatives / Q000191:economics / Q000627:therapeutic use; D035501:Uncertainty
chemical_list: D010975:Platelet Aggregation Inhibitors; D019039:Platelet Glycoprotein GPIIb-IIIa Complex; D000077144:Clopidogrel; D013988:Ticlopidine