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dc.contributor.authorde Bekker-Grob, Esther W
dc.contributor.authorSwait, Joffre D
dc.contributor.authorKassahun, Habtamu Tilahun
dc.contributor.authorBliemer, Michiel CJ
dc.contributor.authorJonker, Marcel F
dc.contributor.authorVeldwijk, Jorien
dc.contributor.authorCong, Karen
dc.contributor.authorRose, John M
dc.contributor.authorDonkers, Bas
dc.date.accessioned2019-11-06T05:48:48Z
dc.date.available2019-11-06T05:48:48Z
dc.date.issued2019
dc.identifier.issn1098-3015
dc.identifier.doi10.1016/j.jval.2019.04.1924
dc.identifier.urihttp://hdl.handle.net/10072/388939
dc.description.abstractBackground: Lack of evidence about the external validity of discrete choice experiments (DCEs) is one of the barriers that inhibit greater use of DCEs in healthcare decision making. Objectives: To determine whether the number of alternatives in a DCE choice task should reflect the actual decision context, and how complex the choice model needs to be to be able to predict real-world healthcare choices. Methods: Six DCEs were used, which varied in (1) medical condition (involving choices for influenza vaccination or colorectal cancer screening) and (2) the number of alternatives per choice task. For each medical condition, 1200 respondents were randomized to one of the DCE formats. The data were analyzed in a systematic way using random-utility-maximization choice processes. Results: Irrespective of the number of alternatives per choice task, the choice for influenza vaccination and colorectal cancer screening was correctly predicted by DCE at an aggregate level, if scale and preference heterogeneity were taken into account. At an individual level, 3 alternatives per choice task and the use of a heteroskedastic error component model plus observed preference heterogeneity seemed to be most promising (correctly predicting >93% of choices). Conclusions: Our study shows that DCEs are able to predict choices—mimicking real-world decisions—if at least scale and preference heterogeneity are taken into account. Patient characteristics (eg, numeracy, decision-making style, and general attitude for and experience with the health intervention) seem to play a crucial role. Further research is needed to determine whether this result remains in other contexts.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom1050
dc.relation.ispartofpageto1062
dc.relation.ispartofissue9
dc.relation.ispartofjournalValue in Health
dc.relation.ispartofvolume22
dc.subject.fieldofresearchPublic Health and Health Services
dc.subject.fieldofresearchApplied Economics
dc.subject.fieldofresearchcode1117
dc.subject.fieldofresearchcode1402
dc.subject.keywordsSocial Sciences
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsEconomics
dc.subject.keywordsHealth Care Sciences & Services
dc.titleAre Healthcare Choices Predictable? The Impact of Discrete Choice Experiment Designs and Models
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationde Bekker-Grob, EW; Swait, JD; Kassahun, HT; Bliemer, MCJ; Jonker, MF; Veldwijk, J; Cong, K; Rose, JM; Donkers, B, Are Healthcare Choices Predictable? The Impact of Discrete Choice Experiment Designs and Models, Value in Health, 2019, 22 (9), pp. 1050-1062
dcterms.dateAccepted2019-04-17
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2019-11-05T05:03:37Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2019, ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorKassahun, Habtamu


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