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Published on: February 12, 2018
Comparing Analytic Hierarchy Process and Discrete-Choice Experiment to Elicit Patient Preferences for Treatment
Marion Danner1, Vera Vennedey1, Mickaël Hiligsmann2
1Institute for Health Economics and Clinical Epidemiology, University Hospital of Cologne (AöR), Cologne, Germany.
Background:
In this study, we conducted an analytic hierarchy process (AHP) and a discrete choice experiment (DCE) to elicit the preferences of patients with age-related macular degeneration using identical attributes and levels.
Objectives:
To compare preference-based weights for age-related macular degeneration treatment attributes and levels generated by two elicitation methods. The properties of both methods were assessed, including ease of instrument use.
Methods:
A DCE and an AHP experiment were designed on the basis of five attributes. Preference-based weights were generated using the matrix multiplication method for attributes and levels in AHP and a mixed multinomial logit model for levels in the DCE. Attribute importance was further compared using coefficient (DCE) and weight (AHP) level ranges. The questionnaire difficulty was rated on a qualitative scale. Patients were asked to think aloud while providing their judgments.
Results:
AHP and DCE generated similar results regarding levels, stressing a preference for visual improvement, frequent monitoring, on-demand and less frequent injection schemes, approved drugs, and mild side effects. Attribute weights derived on the basis of level ranges led to a ranking that was opposite to the AHP directly calculated attribute weights. For example, visual function ranked first in the AHP and last on the basis of level ranges.
Conclusions:
The results across the methods were similar, with one exception: the directly measured AHP attribute weights were different from the level-based interpretation of attribute importance in both DCE and AHP. The dependence/independence of attribute importance on level ranges in DCE and AHP, respectively, should be taken into account when choosing a method to support decision making.
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