Using discrete choice experiments to generate insights into patient treatment preferences

The scenarios might be, for example, descriptions of different treatment profiles. By varying elements of the scenarios systematically and testing responses across a large population, we can explore the interplay of attributes in decision-making and understand what trade-offs people may be willing to make.

We can, for example, learn whether patients prefer Treatment A, which has high efficacy but is administered subcutaneously, or Treatment B, which has slightly lower efficacy but can be taken orally.

We recently ran a multi-country study that employed the DCE methodology to assist an international pharmaceutical company in generating real-world data on patients’ treatment preferences in glioma.

• To ascertain patients’ preferences for the characteristics and outcomes of their treatment.
• Our client wished to gather insights from an international cohort, in a rapid and efficient manner.

• Non-interventional mixed methods patient preference study, collecting data from healthcare professionals and patients on their treatment preferences, and the socio-economic impact of glioma.
• Bespoke data capture across several countries and languages via the Vitaccess Real digital platform.
• Presentation of findings through publication of a manuscript in a reputable journal.
• Integrated eConsent and automated compensation via  Vitaccess Real.

• Generation of patient preference and socioeconomic data from a hard-to-reach patient population, supporting clinical findings for the client’s treatment.
• Rapid gathering of real-world evidence from an international patient population at low cost.
• Crucial contextual data and support for HTAs.

• 181 participants recruited in under 2 months
• 2.5 months from first data entry to analyzed results
• 4 countries
• 97% completion rate

By Emma Bagshaw and Jack Lawrence