A Guide to Measuring and Interpreting Attribute Importance

Stated-preference (SP) methods, such as discrete-choice experiments (DCE) and best–worst scaling (BWS), have increasingly been used to measure preferences for attributes of medical interventions. Preference information is commonly characterized using attribute importance. However, attribute importan...

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Veröffentlicht in:The patient : patient-centered outcomes research Jg. 12; H. 3; S. 287 - 295
1. Verfasser: Gonzalez, Juan Marcos
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.06.2019
Springer Nature B.V
Schlagworte:
Algorithms
Choice Behavior
Data analysis
Delivery of Health Care
Health Administration
Health care
Health Economics
Humans
Intervention
Medical research
Medical treatment
Medicine
Medicine & Public Health
Patient Preference
Pharmacoeconomics and Health Outcomes
Practical Application
Preferences
Public Health
Quality of Life Research
Research Design
ISSN:1178-1653, 1178-1661, 1178-1661
Online-Zugang:Volltext
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Zusammenfassung:Stated-preference (SP) methods, such as discrete-choice experiments (DCE) and best–worst scaling (BWS), have increasingly been used to measure preferences for attributes of medical interventions. Preference information is commonly characterized using attribute importance. However, attribute importance measures  can vary in value and interpretation depending on the method used to elicit preferences, the specific context of the questions, and the approach used to normalize attribute effects. This variation complicates the interpretation of preference results and the comparability of results across subgroups in a sample. This article highlights the potential consequences of ignoring variations in attribute importance measures, and makes the case for reporting more clearly how these measures are obtained and calculated. Transparency in the calculations can clarify what conclusions are supported by the results, and help make more accurate and meaningful comparisons across subsamples.
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ISSN:1178-1653
1178-1661
1178-1661
DOI:10.1007/s40271-019-00360-3