Measuring the Preferences of Patients With Type II Diabetes Using Best-worst Scaling and Discrete Choice Experiment

Study Summary

In 2012, the FDA Center for Devices and Radiological Health (CDRH) issued guidance to clarify the principal benefit-risk factors FDA considers during the reviews for premarket approval applications and de novo classification requests. In addition to a detailed description of benefits and risks, CDRH listed "patient tolerance for risk and perspective on benefit" as a factor that CDRH may consider in regulatory reviews. It underlined the need for developing methods to measure patient preference and incorporate it into regulatory decision-making. The purpose of this study is to advance methods for patient and community engagement in patient-centered outcome research (PCOR) and has three objectives. First, demonstrate good practices for patient and community involvement in PCOR projects by applying principles of community-based participatory research (CBPR). Second, address methodological gaps pertaining to the use of stated-preference methods in studying preferences in PCOR. These include identifying the best methods for designing a preference study and strategies for analyzing variation in preferences. The investigators also seek to assess the relevance of stated-preference methods to patients and stakeholders using both qualitative and quantitative methods. Third, demonstrate good practices for applying stated-preference methods by studying the preferences of patients with type II diabetes. While type II diabetes provides an important case study, this research will advance approaches and methods that will be broadly generalizable to other diseases, and to diverse patient and stakeholder groups. Clinical Significance: This project will illustrate and advance methods for assessing the values of patients and stakeholders. It will demonstrate how CBPR methods apply to PCOR studies and the value of stated-preference methods in measuring the preferences of patients and stakeholders and directing health care.

Primary Outcome

Preference estimate for each medicine attribute level from a conditional logit regression analysis. For each arm, there were six attributes of the medication, with 3 levels each. Within each attribute the parameter estimates for each of the levels sum to 0. If a parameter estimate for a level is above (below) 0 then the parameter is higher (lower) than average for that medication attribute.

Interventions

• OTHER Best-Worst Scaling (Case 2)
• OTHER Discrete Choice Experiment