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      Does MCDA Trump CEA?

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          Multiple criteria decision analysis for health technology assessment.

          Multicriteria decision analysis (MCDA) has been suggested by some researchers as a method to capture the benefits beyond quality adjusted life-years in a transparent and consistent manner. The objectives of this article were to analyze the possible application of MCDA approaches in health technology assessment and to describe their relative advantages and disadvantages. This article begins with an introduction to the most common types of MCDA models and a critical review of state-of-the-art methods for incorporating multiple criteria in health technology assessment. An overview of MCDA is provided and is compared against the current UK National Institute for Health and Clinical Excellence health technology appraisal process. A generic MCDA modeling approach is described, and the different MCDA modeling approaches are applied to a hypothetical case study. A comparison of the different MCDA approaches is provided, and the generic issues that need consideration before the application of MCDA in health technology assessment are examined. There are general practical issues that might arise from using an MCDA approach, and it is suggested that appropriate care be taken to ensure the success of MCDA techniques in the appraisal process. Copyright © 2012 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.
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            Is Open Access

            Distributional Cost-Effectiveness Analysis

            Distributional cost-effectiveness analysis (DCEA) is a framework for incorporating health inequality concerns into the economic evaluation of health sector interventions. In this tutorial, we describe the technical details of how to conduct DCEA, using an illustrative example comparing alternative ways of implementing the National Health Service (NHS) Bowel Cancer Screening Programme (BCSP). The 2 key stages in DCEA are 1) modeling social distributions of health associated with different interventions, and 2) evaluating social distributions of health with respect to the dual objectives of improving total population health and reducing unfair health inequality. As well as describing the technical methods used, we also identify the data requirements and the social value judgments that have to be made. Finally, we demonstrate the use of sensitivity analyses to explore the impacts of alternative modeling assumptions and social value judgments.
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              A pilot study of multicriteria decision analysis for valuing orphan medicines.

              To pilot the use of multicriteria decision analysis to establish and apply a framework of weighted attributes to value orphan medicinal products. Literature searches on the natural history and burden of 40 rare diseases and of how payers assess treatment value and three workshops with, respectively, GlaxoSmithKline managers working on orphan medicinal products, European Union clinical and health economics experts, and representatives of rare diseases patient groups in the European Union. Eight nonmonetary attributes were identified and weights agreed: four concern the disease being treated and four the treatment itself. About half of the weight went to attributes of the disease treated and half to attributes of the treatment. Patient group representatives gave greater weight than did the experts to patients' and carers' quality of daily life. The multicriteria decision analysis approach piloted works and could be developed for use by payers and health technology assessment bodies. © 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Published by International Society for Pharmacoeconomics and Outcomes Research (ISPOR) All rights reserved.
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                Author and article information

                Journal
                Applied Health Economics and Health Policy
                Appl Health Econ Health Policy
                Springer Science and Business Media LLC
                1175-5652
                1179-1896
                April 2018
                February 21 2018
                April 2018
                : 16
                : 2
                : 147-151
                Article
                10.1007/s40258-018-0373-y
                29468578
                9289cfd7-ed0c-4831-88b0-a5a785bb8a93
                © 2018

                http://www.springer.com/tdm

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