{"id":3160,"date":"2024-06-10T11:22:29","date_gmt":"2024-06-10T15:22:29","guid":{"rendered":"https:\/\/cytel.agencyukdev.com\/perspectives\/data-challenges-and-solutions-for-externally-controlled-trials\/"},"modified":"2024-08-21T11:20:22","modified_gmt":"2024-08-21T15:20:22","slug":"data-challenges-and-solutions-for-externally-controlled-trials","status":"publish","type":"perspectives","link":"https:\/\/cytel.agencyukdev.com\/fr\/perspectives\/data-challenges-and-solutions-for-externally-controlled-trials\/","title":{"rendered":"Data Challenges (and Solutions) for Externally Controlled Trials"},"content":{"rendered":"

ClinicReal-world data and evidence are increasingly being used in health care decisions and publications. However, there are challenges in identifying suitable data for external control arms, and researchers need to consider solutions to address issues like data quality, bias, and selection bias when using real-world evidence for comparative efficacy analyses.1<\/a><\/p>\n

Data Challenges for Externally Controlled Trials<\/strong><\/p>\n

Real-world evidence from observational studies has historically had limited use for demonstrating therapeutic effectiveness for regulatory and reimbursement purposes. However, recent developments, such as the 21st Century Cures Act, increased accessibility to large-scale health data, improved standardization of data collection, and regulatory frameworks from bodies like the Food and Drug Administration and European Medicines Agency, have increased the demand for real-world evidence.2<\/p>\n

Real-world data comes from sources like electronic health records, medical claims data, and registries, as well as data from mobile phones, wearables, and patient-reported outcomes.3 However, there are a number of technical challenges raised by regulatory and health reimbursement agencies when evaluating comparative efficacy. These include:<\/p>\n