Results 1 
4 of
4
Accounting for uncertainty in health economic decision models by using model averaging
 J Roy Stat Soc A Sta
"... Summary. Health economic decision models are subject to considerable uncertainty, much of which arises from choices between several plausible model structures, e.g.choices of covariates in a regression model. Such structural uncertainty is rarely accounted for formally in decision models but can be ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
Summary. Health economic decision models are subject to considerable uncertainty, much of which arises from choices between several plausible model structures, e.g.choices of covariates in a regression model. Such structural uncertainty is rarely accounted for formally in decision models but can be addressed by model averaging. We discuss the most common methods of averaging models and the principles underlying them. We apply them to a comparison of two surgical techniques for repairing abdominal aortic aneurysms. In model averaging, competing models are usually either weighted by using an asymptotically consistent model assessment criterion, such as the Bayesian information criterion, or a measure of predictive ability, such as Akaike’s information criterion. We argue that the predictive approach is more suitable when modelling the complex underlying processes of interest in health economics, such as individual disease progression and response to treatment.
A framework for addressing structural uncertainty in decision models
 Medical Decision Making
, 2011
"... Decision analytic models used for health technology assessment are subject to uncertainties. These uncertainties can be quantified probabilistically, by placing distributions on model parameters and simulating from these to generate estimates of costeffectiveness. However, many uncertain model cho ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Decision analytic models used for health technology assessment are subject to uncertainties. These uncertainties can be quantified probabilistically, by placing distributions on model parameters and simulating from these to generate estimates of costeffectiveness. However, many uncertain model choices, often termed structural assumptions, are usually only explored informally by presenting estimates of costeffectiveness under alternative scenarios. The authors show how 2 recent research proposals represent parts of a framework to formally account for all common structural uncertainties. First, the model is expanded to include parameters that encompass all possible structural choices. Uncertainty can then arise because these parameters are estimated imprecisely from data, for example, a treatment effect of doubtful significance. Uncertainty can also arise if there are no relevant data. If there are relevant data, uncertainty can be addressed by averaging expected costs and effects generated from probabilistic analysis of the models with and without the parameter. The weights used for averaging are related to the predictive ability of each model, assessed against the data. If there are no data, additional parameters can often be informed by eliciting expert beliefs as probability distributions. These ideas are illustrated in decision models for antiplatelet therapies for vascular disease and new biologic drugs for the treatment of active psoriatic arthritis. Key words: model averaging; elicitation; probabilistic sensitivity analysis; Bayesian methods. (Med Decis Making 2011;31:662–674)
Acknowledgements iii
, 2007
"... Chapter I: About the NHS Economic Evaluation Database Project 1 ..."
59, Part 2, pp. 233–253 Structural and parameter uncertainty in Bayesian costeffectiveness models
, 2008
"... Summary. Health economic decision models are subject to various forms of uncertainty, including uncertainty about the parameters of the model and about the model structure. These uncertainties can be handled within a Bayesian framework, which also allows evidence from previous studies to be combined ..."
Abstract
 Add to MetaCart
(Show Context)
Summary. Health economic decision models are subject to various forms of uncertainty, including uncertainty about the parameters of the model and about the model structure. These uncertainties can be handled within a Bayesian framework, which also allows evidence from previous studies to be combined with the data. As an example, we consider a Markov model for assessing the costeffectiveness of implantable cardioverter defibrillators. Using Markov chain Monte Carlo posterior simulation, uncertainty about the parameters of the model is formally incorporated in the estimates of expected cost and effectiveness. We extend these methods to include uncertainty about the choice between plausible model structures. This is accounted for by averaging the posterior distributions from the competing models using weights that are derived from the pseudomarginallikelihood and the deviance information criterion, which are measures of expected predictive utility. We also show how these costeffectiveness calculations can be performed efficiently in the widely used software WinBUGS.