Biostatistical Considerations When Using RWD and RWE in Clinical Studies for Regulatory Purposes: A Landscape Assessment. (2nd January 2023)

• Record Type: Journal Article
• Authors: Levenson, Mark; He, Weili; Chen, Jie; Fang, Yixin; Faries, Douglas; Goldstein, Benjamin A.; Ho, Martin; Lee, Kwan; Mishra-Kalyani, Pallavi; Rockhold, Frank; Wang, Hongwei; Zink, Richard C.
• Is Part Of: Statistics in biopharmaceutical research. Volume 15:Number 1(2023)
• Page Start: 3
• Page End: 13
• Publication Date: 2023-01-02
• Subjects: Development -- Medical product -- Regulatory; Pharmacy -- Statistical methods -- Periodicals; Pharmaceutical biotechnology -- Statistical methods -- Periodicals; Biopharmaceutics -- Periodicals; Biometry -- Periodicals; Pharmacy -- Statistical methods; Periodicals; 615.190727
• DOI: 10.1080/19466315.2021.1883473
• Languages: English
• ISSNs: 1946-6315

Abstract

Abstract: Real-world evidence (RWE), derived from data from "real-world" clinical practice and medical product utilization, is an increasingly important source of evidence that holds great potential to increase efficiency and improve clinical development and life cycle management of medical products. Regulatory agencies, public-private partnerships, and health technology assessment organizations have launched major initiatives and released guidance to address considerations in the use of RWE to inform regulatory decision making. However, many challenges remain on how RWE could be best used for various types of regulatory decisions from statistical perspectives. To address the relevant statistical challenges, a working group under the auspices of the ASA Biopharmaceutical Section was established. This article reviews the biostatistical challenges and methods for the use of RWE for medical product development. There are two companion articles as the output from the same working group that address focused topics. The article by Chen et al. provides the current landscape on the use of RWE to inform clinical study design and analysis, and the article by Ho et al. presents a review of causal inference framework for design and analysis of studies using RWE.