Rescaling quality of life values from discrete choice experiments for use as QALYs: a cautionary tale

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dc.contributor.authorFlynn, Terry N
dc.contributor.authorLouviere, Jordan J
dc.contributor.authorMarley, Anthony AJ
dc.contributor.authorCoast, Joanna
dc.contributor.authorPeters, Tim J
dc.date.accessioned2014-08-07T20:48:14Z
dc.date.available2014-08-07T20:48:14Z
dc.date.copyright2008en_US
dc.date.issued2008-10-22
dc.descriptionBioMed Centralen_US
dc.description.abstractBackground: Researchers are increasingly investigating the potential for ordinal tasks such as ranking and discrete choice experiments to estimate QALY health state values. However, the assumptions of random utility theory, which underpin the statistical models used to provide these estimates, have received insufficient attention. In particular, the assumptions made about the decisions between living states and the death state are not satisfied, at least for some people. Estimated values are likely to be incorrectly anchored with respect to death (zero) in such circumstances. Methods: Data from the Investigating Choice Experiments for the preferences of older people CAPability instrument (ICECAP) valuation exercise were analysed. The values (previously anchored to the worst possible state) were rescaled using an ordinal model proposed previously to estimate QALY-like values. Bootstrapping was conducted to vary artificially the proportion of people who conformed to the conventional random utility model underpinning the analyses. Results: Only 26% of respondents conformed unequivocally to the assumptions of conventional random utility theory. At least 14% of respondents unequivocally violated the assumptions. Varying the relative proportions of conforming respondents in sensitivity analyses led to large changes in the estimated QALY values, particularly for lower-valued states. As a result these values could be either positive (considered to be better than death) or negative (considered to be worse than death). Conclusion: Use of a statistical model such as conditional (multinomial) regression to anchor quality of life values from ordinal data to death is inappropriate in the presence of respondents who do not conform to the assumptions of conventional random utility theory. This is clearest when estimating values for that group of respondents observed in valuation samples who refuse to consider any living state to be worse than death: in such circumstances the model cannot be estimated. Only a valuation task requiring respondents to make choices in which both length and quality of life vary can produce estimates that properly reflect the preferences of all respondents.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipAJM was supported by Natural Science and Engineering Research Council Discovery Grant 8124-98 to the University of Victoria.en_US
dc.identifier.citationFlynn et al.: Rescaling quality of life values from discrete choice experiments for use as QALYs: a cautionary tale. Population Health Metrics 2008, 6:6.en_US
dc.identifier.urihttp://www.pophealthmetrics.com/content/6/1/6
dc.identifier.urihttp://dx.doi.org/10.1186/1478-7954-6-6
dc.identifier.urihttp://hdl.handle.net/1828/5523
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.subject.departmentDepartment of Psychology
dc.titleRescaling quality of life values from discrete choice experiments for use as QALYs: a cautionary taleen_US
dc.typeArticleen_US

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