Rescaling quality of life values from discrete choice experiments for use as QALYs: a cautionary tale
Date
2008-10-22
Authors
Flynn, Terry N
Louviere, Jordan J
Marley, Anthony AJ
Coast, Joanna
Peters, Tim J
Journal Title
Journal ISSN
Volume Title
Publisher
BioMed Central
Abstract
Background: 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.
Description
BioMed Central
Keywords
Citation
Flynn et al.: Rescaling quality of life values from discrete choice experiments for use as QALYs: a cautionary tale. Population Health Metrics 2008, 6:6.