Examining the Fitness-for-Purpose of European Real-World Data Sources for External Comparators in Haematological Malignancies

OBJECTIVES: External comparators (ECs) are useful to complement single arm trials by providing real-world (RW) context on comparator treatments, and are increasingly of interest to regulators and payers in rare diseases and their sub-populations. We aimed to understand the fitness-for-purpose of RW data sources (DSs) for haematology-oncology ECs, with the intention of bringing together qualified partners to establish a research collaboration network.

METHODS: Literature reviews, desk research, and recent/ongoing in-house clinical studies were reviewed to identify European DSs involved in RW haematology-oncology data generation. Of 332 European DSs identified, primary market research was conducted with 46 to ascertain in-depth information on patient counts, variable availability, data quality, and operational aspects of data access.

RESULTS: Among the 46 sources, approximately 9531 patients per year were captured across 6 malignancies – DLBCL, FL, CLL, MM, MCL, and MZL – spanning 11 countries. Patient and disease characteristics, labs, clinical outcomes, and treatment pathway were generally readily available (collected in approximately 100%, 87%, 91%, and 91% of sources, respectively). Data quality and completeness was reasonable, but mixed for response measures and prognostic factors, particularly at later lines of therapy. Genetic markers, QoL and HCRU measures were often not readily available (collected in only approximately 46%, 20% and 11% of sources, respectively). Regarding operational aspects, hospitals were more willing than registries and claims databases to share patient-level data with external researchers. Most DSs reported data collection could be conducted via electronic extraction (e.g., electronic medical records, 65%) or a mix of electronic and manual methods (e.g., case report forms, 48%).

CONCLUSIONS: Advances are needed to facilitate RWD capture that matches trial data more closely. Evidence generation programmes that bring together data science and operational expertise, high-quality databases, biobank linkage and natural language processing hold promise for improving data quality and operational efficiency for future haematology-oncology EC study execution.