Fredrik Nyberg1,2, Johan Askling3,4, Niklas Berglind5, Stefan Franzén5, Meilien Ho6, Marie Holmqvist3, Laura Horne7, Kathy Lampl8, Kaleb Michaud9,10, Dimitrios A Pappas11, George Reed12, Deborah Symmons13,14, Eiichi Tanaka15, Trung N Tran16, Suzanne M M Verstappen14, Eveline Wesby-van Swaay17, Hisashi Yamanaka15, Jeffrey D Greenberg18,19. 1. Medical Evidence & Observational Research Centre, Global Medicines Development, AstraZeneca R&D, Mölndal, Sweden. 2. Occupational and Environmental Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 3. Clinical Epidemiology Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden. 4. Department of Rheumatology, Karolinska University Hospital, Stockholm, Sweden. 5. Biometric & Information Sciences, Global Medicines Development, AstraZeneca R&D, Mölndal, Sweden. 6. Clinical, Global Medicines Development, AstraZeneca R&D, Macclesfield, UK. 7. Medical Evidence & Observational Research Centre, Global Medicines Development, AstraZeneca, Wilmington, DE, USA. 8. Clinical, Global Medicines Development, AstraZeneca, Wilmington, DE, USA. 9. University of Nebraska Medical Center, Omaha, NE, USA. 10. National Data Bank for Rheumatic Diseases, Wichita, KS, USA. 11. The College of Physicians and Surgeons, Columbia University Medical Center, New York, NY, USA. 12. University of Massachusetts Medical School, Worcester, MA, USA. 13. NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK. 14. Arthritis Research UK Centre for Epidemiology, The University of Manchester, Manchester, UK. 15. Institute of Rheumatology, Tokyo Women's Medical University, Tokyo, Japan. 16. MedImmune, Gaithersburg, MD, USA. 17. Patient Safety, GRAPSQA, Global Medicines Development, AstraZeneca R&D, Macclesfield, UK. 18. NYU School of Medicine, New York, NY, USA. 19. Corrona LLC, Southborough, MA, USA.
Abstract
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.
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.
Authors: Hisashi Yamanaka; Johan Askling; Niklas Berglind; Stefan Franzen; Thomas Frisell; Christopher Garwood; Jeffrey D Greenberg; Meilien Ho; Marie Holmqvist; Laura Novelli Horne; Eisuke Inoue; Kaleb Michaud; Dimitrios A Pappas; George Reed; Deborah Symmons; Eiichi Tanaka; Trung N Tran; Suzanne M M Verstappen; Eveline Wesby-van Swaay; Fredrik Nyberg Journal: RMD Open Date: 2017-10-10