OBJECTIVES: Patient preferences derived from discrete-choice experiment (DCE) can be used to generate uptake probability. The objective was to calculate uptake for two hypothetical chronic obstructive pulmonary disease (COPD) treatments using data from a pilot study and explore whether DCE-generated preferences can inform cost-effectiveness analysis (CEA) through uptake probabilities to align with goals of patient-centered value assessment.

METHODS: We administered a cross-sectional web-based DCE among US adults with COPD, using Qualtrics^XM. Six attributes, CAT symptom score improvement, doctor response time, medication dose frequency, treatment information source, side effects management, and out-of-pocket cost, were included. The survey questionnaire comprised nine choice tasks (and two hold-out tasks) generated from orthogonal design, symptom questionnaire, and demographics information. The relative preference was estimated using a multinomial logit model. Uptake probability was calculated using established methods for two hypothetical COPD treatments: A (4pt CAT improvement, 2x/daily dosing) and B (6pt CAT improvement, 1x/daily dosing). Out-of-pocket cost was $90 or $120 and other attributes were unchanged.

RESULTS: DCE results (n=30) showed that out-of-pocket cost was the most important attribute with conditional relative importance of (2.65), followed by CAT symptom score improvement (1.46). Medication dose frequency (0.76), treatment information source (0.64), doctor response time (0.21), and side effects management (0.09) were not significant. At $90 out-of-pocket cost for A and B, uptake probability for B was greater than A (79% vs. 21%). At $120 out-of-pocket cost for B, uptake for B decreased but was still greater than A (56% vs. 44%).

CONCLUSIONS: Uptake probability is highly sensitive to out-of-pocket cost. The dynamic influence of out-of-pocket cost on patient acceptance of theoretical COPD treatments (A and B) underscores the potential value of preference information derived from DCE in CEA; population-level uptake of treatments influences the cost-effectiveness ratio. Future research should investigate approaches for integrating uptake into CEA for patient-centered value assessment.