OBJECTIVES: This IMI-PREFER case study explored feasibility and applicability of three preference elicitation methods in rare neuromuscular disorders.

METHODS: Patients and caregivers representing two neuromuscular disorders with similar symptomology completed two online surveys spaced 2 weeks apart. Each survey included a preference elicitation task, either a Q-sort methodology, Best-Worst Scaling Type 2 (BWS), or a Discrete Choice Experiment (DCE), representing different degrees of complexity. Six common attributes (benefits in muscle strength, energy and endurance, balance and cognition and risk for blurry vision or for liver damage) were included, with 3 more added to the Q-sort and attribute levels established for the DCE and the BWS. Five English-speaking countries participated.

RESULTS: n=379 participants responded at least once with n=56 participants completing both BWS and Q-sort, and n=138 completing BWS and DCE. Across the methods, three benefits were consistent with relatively higher values for muscle strength, energy and endurance, and balance. DCE detected risk for liver damage as the attribute of highest value. BWS latent class analysis distinguished between attribute levels with statistically significance in 89% of cases, whereas DCE only in 42%. Q-sort added qualitative statements explaining preferences. Minor differences in preferences were identified when comparing caregivers and patients, but patients with longer disease duration tend to deprioritize risks over benefits at a higher degree. All three methods were feasible to implement, and 80% of those completing the Q-sort, 74% DCEs, and 65% BWS qualified the questions as either ‘very easy’ or ‘easy’ to understand.

CONCLUSIONS: All three methods are feasible and were understood by the sample population. Q-sort allows for more attributes and qualitative information to be collected, BWS is more sensitive in detecting significant differences, and DCE distinguishes framing for benefits and risks. These methods have different applications across the product lifecycle, and researchers should choose the method that best fits their needs.