The Current Landscape in Biostatistics of Real-World Data and Evidence: Causal Inference Frameworks for Study Design and Analysis
Martin Ho, Mark van der Laan, Hana Lee, Jie Chen, Kwan Lee, Yixin Fang, Weili He, Telba Irony, Qi Jiang, Xiwu Lin, Zhaoling Meng, Pallavi S. Mishra‐Kalyani, Frank W. Rockhold, Yang Song, Hongwei Wang, Roseann White
Abstract
As real-world data (RWD) become more readily available, the regulatory agencies, medical product developers, and other key stakeholders have increasing interests in exploring the use of real-world evidence (RWE) to support regulatory decisions alternative to traditional clinical trials. To facilitate and promote statistical research in design, analysis, and interpretation of RWE studies for regulatory decision making, the ASA Biopharmaceutical Section established the RWE Scientific Working Group to address challenges and identify opportunities in the statistical research of this area. This article provides a landscape assessment of relevant causal inference frameworks for study design and analysis that generates RWE. Two companion articles of the Working group review statistical landscape on the use of RWE for medical product label expansion and the other on using RWE to inform clinical trial design and analysis.