Eliciting Distributions to Populate Decision Analytic Models

Background: Elicitation can be used to characterize structural uncertainty within a decision analytic model. This allows the value of acquiring further evidence to resolve these uncertainties to be established. Aim: This article demonstrated the use of expert elicitation for this purpose and also compared the elicited results with the results from alternative assumptions previously used to characterize the uncertainties. Materials and Methods: Distributions for two unknown parameters were elicited. These were used within a model developed to assess the cost-effectiveness of infliximab and etanercept for the treatment of active psoriatic arthritis (PsA), compared with palliative care. The experts' distributions were synthesized using two approaches: linear pooling and random effects meta-analysis. Weighting of experts is also explored. Results: The four methods produce broadly similar results, and in each, the choice of optimum strategy is between etanercept and palliative care (incremental cost-effective ratio for etanercept is between 29,021 pound and 39,259 pound per costs and quality adjusted life years). Decision uncertainty, at a 30,000 pound threshold, is high in all of the synthesis models thus generating high values of further research at between 141 pound and 634 pound million. In each model, the greatest value of further research was for the short-term effectiveness of treatment (47- pound 406 pound million). Discussion: Although the cost-effectiveness results do not differ substantially between the models using the elicited values and the original scenarios, there are some stark contrasts in terms of the values of further research generated. Conclusion: Elicitation offers a feasible method to generate evidence for the missing information but there are a number of key issues for which further research is required.