一致性
检查表
奇纳
背景(考古学)
外部有效性
梅德林
科克伦图书馆
医疗保健
医学
荟萃分析
系统回顾
心理学
社会心理学
护理部
心理干预
认知心理学
内科学
古生物学
经济
生物
法学
经济增长
政治学
作者
Jennifer A. Whitty,Ana Sofia Oliveira Gonçalves
标识
DOI:10.1007/s40271-017-0288-y
摘要
The aim of this study was to compare the acceptability, validity and concordance of discrete choice experiment (DCE) and best-worst scaling (BWS) stated preference approaches in health.A systematic search of EMBASE, Medline, AMED, PubMed, CINAHL, Cochrane Library and EconLit databases was undertaken in October to December 2016 without date restriction. Studies were included if they were published in English, presented empirical data related to the administration or findings of traditional format DCE and object-, profile- or multiprofile-case BWS, and were related to health. Study quality was assessed using the PREFS checklist.Fourteen articles describing 12 studies were included, comparing DCE with profile-case BWS (9 studies), DCE and multiprofile-case BWS (1 study), and profile- and multiprofile-case BWS (2 studies). Although limited and inconsistent, the balance of evidence suggests that preferences derived from DCE and profile-case BWS may not be concordant, regardless of the decision context. Preferences estimated from DCE and multiprofile-case BWS may be concordant (single study). Profile- and multiprofile-case BWS appear more statistically efficient than DCE, but no evidence is available to suggest they have a greater response efficiency. Little evidence suggests superior validity for one format over another. Participant acceptability may favour DCE, which had a lower self-reported task difficulty and was preferred over profile-case BWS in a priority setting but not necessarily in other decision contexts.DCE and profile-case BWS may be of equal validity but give different preference estimates regardless of the health context; thus, they may be measuring different constructs. Therefore, choice between methods is likely to be based on normative considerations related to coherence with theoretical frameworks and on pragmatic considerations related to ease of data collection.
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