A novel approach to augment single-arm clinical studies with real-world data. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Sachdeva, Archie; Tiwari, Ram C; Guha, Subharup
• Is Part Of: Journal of biopharmaceutical statistics. Volume 32:Issue 1(2022)
• Page Start: 141
• Page End: 157
• Publication Date: 2022-01-02
• Subjects: Propensity score -- covariate balanced inference -- real-world data -- real-world evidence -- randomized clinical trial; Pharmacy -- Statistical methods -- Periodicals; Drugs -- Testing -- Statistical methods -- Periodicals; Biometry -- Periodicals; Biopharmaceutics -- Periodicals; Pharmacokinetics -- Periodicals; 615.19
• DOI: 10.1080/10543406.2021.2011902
• Languages: English
• ISSNs: 1054-3406

Abstract

ABSTRACT: In this paper, we develop a methodology for leveraging real-world data into single-arm clinical trial studies. In recent years, the idea of augmenting randomized clinical trials data with real-world data has emerged as a particularly attractive technique for health organizations and drug developers to accelerate the drug development process. Major regulatory authorities such as the Food and Drug Administration and European Medicines Agency have recognized the potential of utilizing real-world data and are advancing toward making regulatory decisions based on real-world evidence. Several statistical methods have been developed in recent years for borrowing data from real-world sources such as electronic health records, product and disease registries, as well as claims and billing data. We propose a novel approach to augment single-arm clinical trials with the real-world data derived from single or multiple data sources. Furthermore, we illustrate the proposed method in the presence of missing data and conduct simulation studies to evaluate its performance in diverse settings.