Discrete Choice Experiment Attribute Selection Using a Multinational Interview Study: Treatment Features Important to Patients with Type 2 Diabetes Mellitus

Abstract

Introduction: Methods for discrete choice experiment (DCE) attribute and attribute-level selection have not yet been firmly established and are rarely reported in detail. This paper describes a qualitative study designed to inform the development of a DCE survey designed to examine preferences for glucagon-like peptide-1 receptor agonist (GLP-1RA) treatments among patients with type 2 diabetes mellitus.

Methods: The study involved a literature review, interviews with clinical experts, and interviews with GLP-1RA-experienced (i.e., exenatide once weekly, liraglutide once daily) and injection-naïve type 2 diabetes patients from Brazil, China, Germany, Japan, and the UK. Interviews followed a semi-structured guide including open-ended questions, and probes to capture the patients' perspective on important aspects of GLP-1RAs and concerns with injectable treatments. Qualitative analyses were performed utilizing MAXQDA version 11. Descriptive statistics were used to summarize the sample.

Results: Thirty-two articles were reviewed, including 13 DCE studies. Thirty-one attributes were identified from the DCE studies, and 61 different attributes were identified in the non-DCE studies. Fifty patients completed interviews: 28 (56%) GLP-1RA-experienced and 22 (44%) injection-naïve, 54% were male, and they had a mean age of 52.8 years. Patients considered efficacy, adverse effects, and dosing frequency as the most important treatment-related attributes. From a list of five pre-defined device- or regimen-related attributes, patients considered dosing frequency, needle size, and injection preparation as the three most important attributes.

Conclusion: This study adds to the DCE literature to inform researchers on attribute selection methodology in an international setting as there is limited published information to guide researchers on best practices for selecting and defining attributes for DCE surveys.