Nominal Group Technique to Select Attributes for Discrete Choice Experiments: An Example for Drug Treatment Choices in Osteoporosis

Background: Attribute selection represents an important step in the development of discrete- choice experiments (DCEs), but is often poorly reported. In some situations, the number of attributes identified may exceed what one may find possible to pilot in a DCE. Hence, there is a need to gain insight into methods to select attributes in order to construct the final list of attributes. This study aims to test the feasibility of using the nominal group technique (NGT) to select attributes for DCEs. Methods: Patient group discussions (4-8 participants) were convened to prioritize a list of 12 potentially important attributes for osteoporosis drug therapy. The NGT consisted of three steps: an individual ranking of the 12 attributes by importance from 1 to 12, a group discussion on each of the attributes, including a group review of the aggregate score of the initial rankings, and a second ranking task of the same attributes. Results: Twenty-six osteoporotic patients participated in five NGT sessions. Most (80%) of the patients changed their ranking after the discussion. However, the average initial and final ranking did not differ markedly. In the final ranking, the most important medication attributes were effectiveness, side effects, and frequency and mode of administration. Some (15%) of the patients did not correctly rank from 1 to 12, and the order of attributes did play a role in the ranking. Conclusion: The NGT is feasible for selecting attributes for DCEs. Although in the context of this study, the NGT session had little impact on prioritizing attributes, this approach is rigorous, transparent, and improves the face validity of DCEs. Additional research in other contexts (different decisional problems or different diseases) is needed to determine the added value of the NGT session, to assess the optimal ranking/rating method with control of ordering effects, and to compare the attributes selected with the different approaches.