The Value of Federated Data Networks in Oncology: What Research Questions Do They Answer? Outcomes From a Systematic Literature Review

• Published in: Value in health Vol. 25; no. 5; pp. 855 - 868
• Main Authors: Hunger, Matthias, Bardenheuer, Kristina, Passey, Alun, Schade, René, Sharma, Ruchika, Hague, Clare
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2022
• Subjects: common data model; data analysis; Data Collection; evidence generation; federated data network; Humans; Medical Oncology; Neoplasms - therapy; Observational Medical Outcomes Partnership; oncology; real-world evidence; systematic literature review; common data model; Observational Medical Outcomes Partnership; data analysis; real-world evidence; evidence generation; federated data network; oncology; systematic literature review
• ISSN: 1098-3015; 1524-4733
• DOI: 10.1016/j.jval.2021.11.1357

Real-world evidence (RWE) plays an important role in addressing key research questions of interest to healthcare decision makers. Federated data networks (FDNs) apply novel technology to enable the conduct of RWE studies with multiple partners, without the need to share the individual partner’s data set. A systematic review of the published literature was performed to determine which types of research questions can best be addressed through FDNs, specifically in the field of oncology. Systematic searches of MEDLINE and Embase were undertaken to identify the types of research questions that had been addressed in studies using FDNs. Additional information was retrieved about study characteristics, statistical methods, and the FDN itself. In total, 40 publications were included where research questions on the following had been addressed (multiple categories possible): disease natural history (58%), safety surveillance (18%), treatment pathways (15%), comparative effectiveness (10%), and cost/resource use studies (3%)—13% of studies had to be left uncategorized. A total of 50% of the studies were run with data partners in networks of ≤5. The size of the networks ranged from 227 patients to >5 million patients. Statistical methods used included distributed learning and distributed regression methods. Further work is needed to raise awareness of the important role that FDNs can play in leveraging readily available RWE to address key research questions of interest in cancer and the benefits to the research community in engaging in federated data initiatives with a long-term perspective.