An inappropriate structure for a decision analytic model can potentially invalidate estimates of
cost-effectiveness and estimates of the value of further research. However, there are often a
number of alternative and credible structural assumptions which can be made. Although it is
common practice to acknowledge potential limitations in model structure, there is a lack of
clarity about methods to characterize the uncertainty surrounding alternative structural
assumptions and their contribution to decision uncertainty.
A review of decision models commissioned by the NHS Health Technology Programme was
undertaken to identify the types of model uncertainties described in the literature. A second
review was undertaken to identify approaches to characterise these uncertainties.
The assessment of structural uncertainty has received little attention in the health economics
literature. A common method to characterise structural uncertainty is to compute results for
each alternative model specification, and to present alternative results as scenario analyses.
It is then left to decision maker to assess the credibility of the alternative structures in
interpreting the range of results.
The review of methods to explicitly characterise structural uncertainty identified two methods:
1) model averaging, where alternative models, with different specifications, are built, and their
results averaged, using explicit prior distributions often based on expert opinion and 2) Model
selection on the basis of prediction performance or goodness of fit. For a number of reasons
these methods are neither appropriate nor desirable methods to characterize structural
uncertainty in decision analytic models.
When faced with a choice between multiple models, another method can be employed which
allows structural uncertainty to be explicitly considered and does not ignore potentially
relevant model structures. Uncertainty can be directly characterised (or parameterised) in the
model itself. This method is analogous to model averaging on individual or sets of model
inputs, but also allows the value of information associated with structural uncertainties to be
|CHE Research Paper
|Centre for Health Economics
© 2006 Laura Bojke, Karl Claxton, Stephen Palmer, Mark Sculpher. The full text of this report can be viewed free of charge from the Centre for Health Economics web site at: http://www.york.ac.uk/inst/che/pdf/rp9.pdf