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One Method, Many Methodological Choices: A Structured Review of Discrete-Choice Experiments for Health State Valuation

Brendan Mulhern (), Richard Norman, Deborah J. Street and Rosalie Viney
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Brendan Mulhern: University of Technology
Richard Norman: Curtin University
Deborah J. Street: University of Technology
Rosalie Viney: University of Technology

PharmacoEconomics, 2019, vol. 37, issue 1, 29-43

Abstract: Abstract Background Discrete-choice experiments (DCEs) are used in the development of preference-based measure (PBM) value sets. There is considerable variation in the methodological approaches used to elicit preferences. Objective Our objective was to carry out a structured review of DCE methods used for health state valuation. Methods PubMed was searched until 31 May 2018 for published literature using DCEs for health state valuation. Search terms to describe DCEs, the process of valuation and preference-based instruments were developed. English language papers with any study population were included if they used DCEs to develop or directly inform the production of value sets for generic or condition-specific PBMs. Assessment of paper quality was guided by the recently developed Checklist for Reporting Valuation Studies. Data were extracted under six categories: general study information, choice task and study design, type of designed experiment, modelling and analysis methods, results and discussion. Results The literature search identified 1132 published papers, and 63 papers were included in the review. Paper quality was generally high. The study design and choice task formats varied considerably, and a wide range of modelling methods were employed to estimate value sets. Conclusions This review of DCE methods used for developing value sets suggests some recurring limitations, areas of consensus and areas where further research is required. Methodological diversity means that the values should be seen as experimental, and users should understand the features of the value sets produced before applying them in decision making.

Date: 2019
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