Unknown

Dataset Information

0

Empirical validation of patient versus population preferences in calculating QALYs.


ABSTRACT: A fundamental assumption of the quality-adjusted life year model is mutual utility independence between life years and health status. However, this assumption may not hold for severe health states: living in a severe health state may cause disutility beyond a so-called maximal endurable time (MET). It is unknown, however, whether persons without experience of a disease, who are often used in health state valuation exercises, account for MET. Using data from 159 respondents from two convenience samples in Germany who were presented a health state description of depression, this study shows that persons without experience of depression had a lower rate of MET than persons with a history of depression. Furthermore, they had more preference reversals in case of MET, thus violating a fundamental principle of rational choice theory. While these findings suggest that severe health states should be assessed by patients rather than the community, confirmation in additional studies outside Germany and based on other health-state valuation techniques and diseases is recommended.

SUBMITTER: Weyler EJ 

PROVIDER: S-EPMC3207192 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

altmetric image

Publications

Empirical validation of patient versus population preferences in calculating QALYs.

Weyler Eva-Julia EJ   Gandjour Afschin A  

Health services research 20110421 5


A fundamental assumption of the quality-adjusted life year model is mutual utility independence between life years and health status. However, this assumption may not hold for severe health states: living in a severe health state may cause disutility beyond a so-called maximal endurable time (MET). It is unknown, however, whether persons without experience of a disease, who are often used in health state valuation exercises, account for MET. Using data from 159 respondents from two convenience s  ...[more]

Similar Datasets

| S-EPMC6676435 | biostudies-literature
| S-EPMC6710130 | biostudies-literature
| S-EPMC3406057 | biostudies-literature
| S-EPMC7058914 | biostudies-literature
| S-EPMC11002318 | biostudies-literature
| S-EPMC5541334 | biostudies-literature
| S-EPMC5321749 | biostudies-literature
| S-EPMC2394959 | biostudies-literature
2014-11-13 | GSE62116 | GEO
| S-EPMC4703247 | biostudies-literature