Using Nominal Group Technique to Identify Key Attributes of Oncology Treatments for a Discrete Choice Experiment.

Responding to rising oncology therapy costs, multiple value frameworks are emerging. However, input from economists in their design and conceptualization has been limited, and no existing framework has been developed using preference weightings as legitimate indicators of value. This article outlines use of the nominal group technique to identify valued treatment attributes (such as treatment inconvenience) and contextual considerations (such as current life expectancy) to inform the design of a discrete choice experiment to develop a preference weighted value framework for future decision makers. Three focus groups were conducted in 2017 with cancer patients, oncology physicians, and nurses. Using the nominal group technique, participants identified and prioritized cancer therapy treatment and delivery attributes as well as contextual issues considered when choosing treatment options. Focus groups with patients ( = 8), physicians ( = 6), and nurses ( = 10) identified 30 treatment attributes and contextual considerations. Therapy health gains was the first priority across all groups. Treatment burden/inconvenience to patients and their families and quality of evidence were prioritized treatment attributes alongside preferences for resource use and cost (to patients and society) attributes. The groups also demonstrated that contextual considerations when choosing treatment varied across the stakeholders. Patients prioritized existence of alternative treatments and oncologist/center reputation while nurses focused on administration harms, communication, and treatment innovation. The physicians did not prioritize any contextual issues in their top rankings. The study demonstrates that beyond health gains, there are treatment attributes and contextual considerations that are highly prioritized across stakeholder groups. These represent important candidates for inclusion in a discrete choice experiment seeking to provide weighted preferences for a value framework for oncology treatment that goes beyond health outcomes.