News Updates
- Benefits of Integrating Patient Engagement with Diversity, Equity, and Inclusion in New Medical Product Development: A Call for Action
- Drug Shortages in the Global South: A Proposed Parallel Tech and Reg Transfer Framework
- Fair Pay for Patient Engagement: Navigating the Evolving Landscape of Remuneration
- Industry’s Window to Express Interest in Africa Continental Product Evaluation Pilot Closes End of February 2024
- Accelerating Adoption of eLabeling in Singapore: One Company’s Journey
Postmarket Assessment: Erythropoiesis Stimulators
Postmarketing Benefit-Risk Assessment for Erythropoiesis-Stimulating Agents Using a Health Care Database
Background: While benefit-risk (B-R) assessment in the real-world setting is an important challenge for pharmacovigilance, few studies have explored this approach. To investigate the utility and limitations of B-R assessment using a health care database by applying the Benefit Risk Action Team (BRAT) framework, we have conducted a case study with erythropoietin agents.
Methods: Postmarketing data from the Medical Data Vision health care database were used in a B-R comparison between methoxy polyethylene glycol-epoetin beta (continuous erythropoietin receptor activator; C.E.R.A.) and other erythropoiesis-stimulating agents (ESAs). Data were from patients with chronic kidney disease (CKD) treated with C.E.R.A. (n = 131: nondialysis, 109; hemodialysis, 22) or other ESAs (n = 542: nondialysis, 327; hemodialysis, 215) between July 2011 and March 2014.
Results: The B-R profile for C.E.R.A. appeared to be similar to that for other ESAs in both nondialysis and hemodialysis patients with CKD, when benefits and risks were mainly assessed in terms of odds ratios. Despite various point estimates and confidence intervals for each outcome, the results of subgroup analyses showed no notable differences from the overall analysis in B-R assessment.
Conclusions: B-R assessment can be performed using the BRAT framework with a health care database, but limitations exist when using a single data source. Care should be taken when selecting data for extraction and defining outcomes of interest. Further research is necessary to facilitate practical application of this approach.