Ontology type: schema:ScholarlyArticle Open Access: True
2008-08
AUTHORSEmily Lancsar, Jordan Louviere
ABSTRACTDiscrete choice experiments (DCEs) are regularly used in health economics to elicit preferences for healthcare products and programmes. There is growing recognition that DCEs can provide more than information on preferences and, in particular, they have the potential to contribute more directly to outcome measurement for use in economic evaluation. Almost uniquely, DCEs could potentially contribute to outcome measurement for use in both cost-benefit and cost-utility analysis. Within this expanding remit, our intention is to provide a resource for current practitioners as well as those considering undertaking a DCE, using DCE results in a policy/commercial context, or reviewing a DCE. We present the fundamental principles and theory underlying DCEs. To aid in undertaking and assessing the quality of DCEs, we discuss the process of carrying out a choice study and have developed a checklist covering conceptualizing the choice process, selecting attributes and levels, experimental design, questionnaire design, pilot testing, sampling and sample size, data collection, coding of data, econometric analysis, validity, interpretation and welfare and policy analysis. In this fast-moving area, a number of issues remain on the research frontier. We therefore outline potentially fruitful areas for future research associated both with DCEs in general, and with health applications specifically, paying attention to how the results of DCEs can be used in economic evaluation. We also discuss emerging research trends. We conclude that if appropriately designed, implemented, analysed and interpreted, DCEs offer several advantages in the health sector, the most important of which is that they provide rich data sources for economic evaluation and decision making, allowing investigation of many types of questions, some of which otherwise would be intractable analytically. Thus, they offer viable alternatives and complements to existing methods of valuation and preference elicitation. More... »
PAGES661-677
http://scigraph.springernature.com/pub.10.2165/00019053-200826080-00004
DOIhttp://dx.doi.org/10.2165/00019053-200826080-00004
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1008806799
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/18620460
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JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.2165/00019053-200826080-00004'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.2165/00019053-200826080-00004'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.2165/00019053-200826080-00004'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.2165/00019053-200826080-00004'
This table displays all metadata directly associated to this object as RDF triples.
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