Presentation (CTI)

OBJECTIVES: Discrete Choice Experiments (DCEs) are increasingly recognized as a robust method for quantifying treatment preferences and enhancing the interpretability of real-world evidence (RWE). When combined with precision-patient-finding enabled with medical and pharmaceutical claims, DCEs can be used to understand treatment preferences in rare diseases and amongst specific patient groups providing even more valuable insights. By systematically eliciting trade-offs individuals are willing to make among competing attributes—such as efficacy, safety and convenience—DCEs provide insights into value perceptions that traditional RWE sources may overlook. The objective of this research was to understand how patient treatment preferences were explored, and DCEs used, in rare disease areas, specifically non-small cell lung cancer (NSCLC).
METHODS: A targeted literature review of published studies indexed in the PubMed bibliographic database from 01 January 2015 to 12 December 2024 was conducted. Studies identified evaluated patient preferences regarding treatment modalities in patients diagnosed with NSCLC. Only studies conducted in the United States (or involving a subset of US patients/caregivers) were included.
RESULTS: 2891 studies were initially identified. Title and abstract screening were performed to determine the relevance of each study. 42 studies were selected for full-text review. After the full-text review, 11 eligible studies were identified. Ten used DCE methodology to elicit preferences.
CONCLUSIONS: As the healthcare landscape shifts toward personalized and value-based care, DCEs serve as a critical bridge between clinical outcomes and stakeholder-relevant value, enriching the evidence base used to guide policy, access, and resource allocation decisions. Unfortunately, few DCEs are utilized in rare disease states. For HEOR professionals, increased use of DCEs would compliment observational and claims-based analyses, enabling the integration of patient and provider voices into budget impact models, benefit-risk assessments, etc. Their more frequent application would support patient-centered value frameworks, strengthens submissions to approval agencies and could be used to inform pricing and coverage strategies.