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We advocate an approach of limiting candidate models to those that are clinically plausible, and then use the Occam's window method [46] of only averaging over models that have a non-negligible weight (a probability P [https://en.wikipedia.org/wiki/Phosphoprotein_phosphatase Phosphoprotein phosphatase] from two trials comparing treatments E versus F and G versus H, which were external to the decision strategy space, to inform the model selection process [8]. We found that the averaged model was dominated by the HI model, which was accorded a probability of 0.94 on the smoothed DIC criterion. Even in this case incorporating structural uncertainty has a very large influence on the EVPI increasing it relative to the HI model by 20- to 30-fold over the range of cost-effectiveness thresholds commonly considered to be appropriate for the United Kingdom. This is because even though there is little model uncertainty, the consequences of using a different model are high. Our results are consistent with the findings of Bojke et al. [19] who found that when equal model weights were used the EVPI estimated using model averaging differed by a factor of up to 35 when compared to the lowest EVPI [http://www.selleckchem.com/products/Bafilomycin-A1.html Bafilomycin A1 cell line] of the candidate models. However, in principle model averaging could reduce decision uncertainty and EVPI compared to the best fitting model if model averaging were to push the incremental net benefit further [http://www.selleckchem.com/products/pf-06463922.html PF-06463922 order] away from the decision threshold. The methodology presented can be readily extended to the computation of expected value of partial perfect information and expected value of sample information by adding an additional layer of expectation over candidate models, A, around the expectation over model parameters, ��a, in the corresponding formulae. However, we would expect this additional layer of expectation to add to the computational burden of these often computationally expensive calculations. This article shows that alternative parameterizations of treatment effects in Markov models of disease progression can have an extreme affect on decision uncertainty and the expected value of further research, and even on reimbursement decisions. This structural uncertainty was accounted for using Bayesian model averaging and a formula for estimating the EVPI within a model-averaging framework is introduced. The authors thank the reviewers for their helpful comments and suggestions.