Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/115539
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chen, G. | - |
dc.contributor.author | Khan, M. | - |
dc.contributor.author | Iezzi, A. | - |
dc.contributor.author | Ratcliffe, J. | - |
dc.contributor.author | Richardson, J. | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Medical Decision Making, 2016; 36(2):160-175 | - |
dc.identifier.issn | 0272-989X | - |
dc.identifier.issn | 1552-681X | - |
dc.identifier.uri | http://hdl.handle.net/2440/115539 | - |
dc.description.abstract | Background: Cost-utility analyses commonly employ a multiattribute utility (MAU) instrument to estimate the health state utilities, which are needed to calculate quality-adjusted life years. Different MAU instruments predict significantly different utilities, which makes comparison of results from different evaluation studies problematical. Aim: This article presents mapping functions (‘‘crosswalks’’) from 6 MAU instruments (EQ-5D-5L, SF- 6D, Health Utilities Index 3 [HUI 3], 15D, Quality of Well- Being [QWB], and Assessment of Quality of Life 8D [AQoL-8D]) to each of the other 5 instruments in the study: a total of 30 mapping functions. Methods: Data were obtained from a multi-instrument comparison survey of the public and patients in 7 disease areas conducted in 6 countries (Australia, Canada, Germany, Norway, United Kingdom, and United States). The 8022 respondents were administered each of the 6 study instruments. Mapping equations between each instrument pair were estimated using 4 econometric techniques: ordinary least squares, generalized linear model, censored least absolute deviations, and, for the first time, a robust MM-estimator. Results: Goodness-of-fit indicators for each of the results are within the range of published studies. Transformations reduced discrepancies between predicted utilities. Incremental utilities, which determine the value of quality-related health benefits, are almost perfectly aligned at the sample means. Conclusion: Transformations presented here align the measurement scales of MAU instruments. Their use will increase confidence in the comparability of evaluation studies, which have employed different MAU instruments. | - |
dc.description.statementofresponsibility | Gang Chen, Munir A. Khan, Angelo Iezzi, Julie Ratcliffe, Jeff Richardson | - |
dc.language.iso | en | - |
dc.publisher | SAGE Publications | - |
dc.rights | © The Author(s) 2015 | - |
dc.source.uri | http://dx.doi.org/10.1177/0272989x15578127 | - |
dc.subject | Cost-utility analysis; cost effectiveness analysis; health-related quality of life; utility; mapping | - |
dc.title | Mapping between 6 multiattribute utility instruments | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1177/0272989X15578127 | - |
dc.relation.grant | http://purl.org/au-research/grants/nhmrc/1006334 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Ratcliffe, J. [0000-0001-7365-1988] | - |
Appears in Collections: | Aurora harvest 3 Public Health publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.