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 importance measures can vary in value and interpretation depending on the method used to elicit preferences, the specific contextof the questions, and the approach used to normalize attributeeffects. This variationcomplicates the interpretation ofpreference resultsandthe comparability of results across subgroups in a sample. This article highlightsthe potentialconsequences of ignoring variations in attribute importance measures, and makes the case for reportingmore clearly how these measures are obtained andcalculated.Transparency inthecalculations canclarify what conclusions aresupported by the results, and helpmake more accurate and meaningful comparisons across subsamples.