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Uncertainty and validation of health economic decision models

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  • Lois G. Kim
  • Simon G. Thompson

Abstract

Health economic decision models are based on specific assumptions relating to model structure and parameter estimation. Validation of these models is recommended as an indicator of reliability, but is not commonly reported. Furthermore, models derived from different data and employing different assumptions may produce a variety of results. A Markov model for evaluating the long‐term cost‐effectiveness of screening for abdominal aortic aneurysm is described. Internal, prospective and external validations are carried out using individual participant data from two randomised trials. Validation is assessed in terms of total numbers and timings of key events, and total costs and life‐years. Since the initial model validates well only internally, two further models are developed that better fit the prospective and external validation data. All three models are then extrapolated to a life‐time horizon, producing cost‐effectiveness estimates ranging from £1600 to £4200 per life‐year gained. Parameter uncertainty is now commonly addressed in health economic decision modelling. However, the derivation of models from different data sources adds another level of uncertainty. This extra uncertainty should be recognised in practical decision‐making and, where possible, specifically investigated through independent model validation. Copyright © 2009 John Wiley & Sons, Ltd.

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  • Lois G. Kim & Simon G. Thompson, 2010. "Uncertainty and validation of health economic decision models," Health Economics, John Wiley & Sons, Ltd., vol. 19(1), pages 43-55, January.
    • Handle: RePEc:wly:hlthec:v:19:y:2010:i:1:p:43-55
    DOI: 10.1002/hec.1444
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    References listed on IDEAS

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