Background: Standard approaches to estimation of Markov models with data from randomized controlled trials tend either to make a judgment about which transition(s) treatments act on, or they assume that treatment has a separate effect on every transition. An alternative is to fit a series of models that assume that treatment acts on specific transitions. Investigators can then choose among alternative models using goodness-of-fit statistics. However, structural uncertainty about any chosen parameterization will remain and this may have implications for the resulting decision and the need for further research. Methods: Wedescribe a Bayesian approach to model estimation, and model selection. Structural uncertainty about which parameterization to use is accounted for using model averaging and we developed a formula for calculating the expected value of perfect information (EVPI) in averaged models. Marginal posterior distributions are generated for each of the cost-effectiveness parameters using Markov Chain Monte Carlo simulation in WinBUGS, or Monte-Carlo simulation in Excel (Microsoft Corp., Redmond, WA).Weillustrate the approach with an example of treatments for asthma using aggregate-level data from a connected network of four treatments compared in three pair-wise randomized controlled trials. Results: The standard errors of incremental net benefit using structured models is reduced by up to eight- or ninefold compared to the unstructured models, and the expected loss attaching to decision uncertainty by factors of several hundreds. Model averaging had considerable influence on the EVPI. Conclusions: Alternative structural assumptions can alter the treatment decision and have an overwhelming effect on model uncertainty and expected value of information. Structural uncertaintycanbeaccountedforbymodelaveraging, andtheEVPIcanbecalculated foraveragedmodels. Copyright © 2011, International Society for Pharmacoeconomics and Outcomes Research (ISPOR).
Price, M. J., Welton, N. J., Briggs, A. H., & Ades, A. E. (2011). Model averaging in the presence of structural uncertainty about treatment effects: Influence on treatment decision and expected value of information. Value in Health, 14(2), 205–218. https://doi.org/10.1016/j.jval.2010.08.001