Using epidemiological registry data to provide background rates as context for adverse events in a rheumatoid arthritis drug development program: A coordinated approach

Purpose: Observational studies can provide context for adverse events observed in clinical trials, especially for infrequent events or long-term risks. We developed methods to improve safety contextualization for a rheumatoid arthritis drug development program through coordinated analyses of multiple registries. Methods: We identified and characterized differences and similarities across five registries (Swedish Rheumatology Quality of Care Register, Consortium of Rheumatology Researchers of North America [CORRONA], Norfolk Arthritis Register, Institute of Rheumatology Rheumatoid Arthritis, and the new CORRONA International), harmonized outcome definitions, and investigated whether restricted subcohorts improved comparability with trial populations. To address confounding, we identified risk predictors for outcomes of interest (mortality, cardiovascular disease, infection, and malignancy). We used patient-level analyses at each registry and central analysis of standardized group-level data. Results: Despite data differences, the coordinated approach enabled consistent variable definitions for key baseline characteristics and outcomes. Selection of restricted subcohorts (e.g., using active joint count criteria) improved baseline comparability with trial patients for some rheumatoid arthritis disease activity measures, but less for other characteristics (e.g., age and comorbidity); however, such selection decreased sample size considerably. For most outcomes, age was the most important risk predictor, emphasizing the importance of age/sex standardization to address confounding. The prospective approach enabled use of recent relevant data; the distributed analysis safeguarded confidentiality of registry data. Conclusions: Compared with reliance on published data alone, a forward-looking coordinated approach across multiple observational data sources can improve comparability and consistency and better support sensitivity analyses and data interpretation, in contextualizing safety data from clinical trials. This approach may have utility to support safety assessments across diverse diseases and drug development programs and satisfy future regulatory requirements.