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Abstract
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 goodnessoffit 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: We describe 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 costeffectiveness parameters using Markov Chain Monte Carlo simulation in WinBUGS, or MonteCarlo simulation in Excel (Microsoft Corp., Redmond, WA). We illustrate the approach with an example of treatments for asthma using aggregatelevel data from a connected network of four treatments compared in three pairwise randomized controlled trials.
Results: The standard errors of incremental net benefit using structured models is reduced by up to eightor 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 uncertainty can be accounted for by model averaging, and the EVPI can be calculated for averaged models. Copyright (C) 2011, International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc.
Original language  English 

Pages (fromto)  205218 
Number of pages  14 
Journal  Value in Health 
Volume  14 
Issue number  2 
DOIs  
Publication status  Published  2011 
Structured keywords
 ConDuCTII
Keywords
 CONCEPTUALFRAMEWORK
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Dive into the research topics of 'Model Averaging in the Presence of Structural Uncertainty about Treatment Effects: Influence on Treatment Decision and Expected Value of Information'. Together they form a unique fingerprint.Projects
 1 Finished

COLLABORATION AND INNOVATION IN DIFFICULT OR RANDOMISED CONTROLLED TRIALS
1/04/09 → 1/04/14
Project: Research