Discrete choice experiments to generate utility values for multi-attribute utility instruments: a systematic review of methods.

Bahrampour, M; Byrnes, J; Norman, R; Scuffham, PA; Downes, M

Discrete choice experiments to generate utility values for multi-attribute utility instruments: a systematic review of methods.

Bahrampour, M

Byrnes, J Norman, R Scuffham, PA Downes, M

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Eur J Health Econ, 2020, 21, (7), pp. 983-992
Issue Date:: 2020-09

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Field	Value	Language
dc.contributor.author	Bahrampour, M https://orcid.org/0000-0002-6915-1865
dc.contributor.author	Byrnes, J
dc.contributor.author	Norman, R
dc.contributor.author	Scuffham, PA
dc.contributor.author	Downes, M
dc.date.accessioned	2022-10-30T22:13:26Z
dc.date.available	2020-04-15
dc.date.available	2022-10-30T22:13:26Z
dc.date.issued	2020-09
dc.identifier.citation	Eur J Health Econ, 2020, 21, (7), pp. 983-992
dc.identifier.issn	1618-7598
dc.identifier.issn	1618-7601
dc.identifier.uri	http://hdl.handle.net/10453/162986
dc.description.abstract	OBJECTIVES: In recent years, discrete choice experiments (DCEs) have become frequently used to generate utility values, but there are a diverse range of approaches to do this. The primary focus of this systematic review is to summarise the methods used for the design and analysis of DCEs when estimating utility values in both generic and condition-specific preference-based measures. METHODS: Published literature using DCEs to estimate utility values from preference-based instruments were identified from MEDLINE, Embase, Cochrane Library and CINAHL using PRISMA guidelines. To assess the different DCE methods, standardised information was extracted from the articles including the DCE design method, the number of choice sets, the number of DCE pairs per person, randomisation of questions, analysis method, logical consistency tests and techniques for anchoring utilities. The CREATE checklist was used to assess the quality of the studies. RESULTS: A total of 38 studies with samples from the general population, students and patients were included. Values for health states described using generic multi attribute instruments (MAUIs) (especially the EQ-5D) were the most commonly explored using DCEs. The studies showed considerable methodology and design diversity (number of alternatives, attributes, sample size, choice task presentation and analysis). Despite these differences, the quality of articles reporting the methods used for the DCE was generally high. CONCLUSION: DCEs are an important approach to measure utility values for both generic and condition-specific instruments. However, a gold standard method cannot yet be recommended.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	SPRINGER
dc.relation.ispartof	Eur J Health Econ
dc.relation.isbasedon	10.1007/s10198-020-01189-6
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1117 Public Health and Health Services, 1402 Applied Economics
dc.subject.classification	Health Policy & Services
dc.subject.mesh	Choice Behavior
dc.subject.mesh	Cost-Benefit Analysis
dc.subject.mesh	Health Status
dc.subject.mesh	Humans
dc.subject.mesh	Patient Preference
dc.subject.mesh	Quality of Life
dc.subject.mesh	Random Allocation
dc.subject.mesh	Research Design
dc.subject.mesh	Humans
dc.subject.mesh	Random Allocation
dc.subject.mesh	Choice Behavior
dc.subject.mesh	Health Status
dc.subject.mesh	Research Design
dc.subject.mesh	Quality of Life
dc.subject.mesh	Cost-Benefit Analysis
dc.subject.mesh	Patient Preference
dc.title	Discrete choice experiments to generate utility values for multi-attribute utility instruments: a systematic review of methods.
dc.type	Journal Article
utslib.citation.volume	21
utslib.location.activity	Germany
utslib.for	1117 Public Health and Health Services
utslib.for	1402 Applied Economics
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Faculty of Health/Centre for Health Economics Research and Evaluation
utslib.copyright.status	closed_access	*
dc.date.updated	2022-10-30T22:13:25Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	7

Abstract:

OBJECTIVES: In recent years, discrete choice experiments (DCEs) have become frequently used to generate utility values, but there are a diverse range of approaches to do this. The primary focus of this systematic review is to summarise the methods used for the design and analysis of DCEs when estimating utility values in both generic and condition-specific preference-based measures. METHODS: Published literature using DCEs to estimate utility values from preference-based instruments were identified from MEDLINE, Embase, Cochrane Library and CINAHL using PRISMA guidelines. To assess the different DCE methods, standardised information was extracted from the articles including the DCE design method, the number of choice sets, the number of DCE pairs per person, randomisation of questions, analysis method, logical consistency tests and techniques for anchoring utilities. The CREATE checklist was used to assess the quality of the studies. RESULTS: A total of 38 studies with samples from the general population, students and patients were included. Values for health states described using generic multi attribute instruments (MAUIs) (especially the EQ-5D) were the most commonly explored using DCEs. The studies showed considerable methodology and design diversity (number of alternatives, attributes, sample size, choice task presentation and analysis). Despite these differences, the quality of articles reporting the methods used for the DCE was generally high. CONCLUSION: DCEs are an important approach to measure utility values for both generic and condition-specific instruments. However, a gold standard method cannot yet be recommended.

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http://hdl.handle.net/10453/162986