Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, USA
Research Article
Statistical Evaluation of the Validity of Real-World Data and Real-World Evidence
Author(s): Yuankang Zhao* and Shein-Chung Chow
Real-world data (RWD) often consist of positive or negative studies and the data may be structured or unstructured. In this case, the validity of realworld
evidence (RWE) that derived from RWD is a concern for providing substantial evidence regarding the safety and efficacy of the test treatment
under investigation. The validity of RWD/RWE is essential, especially when it is intended to support regulatory submission. In practice, studies with
positive results are more likely accepted in RWD, which may cause substantial selection bias. In this article, a quantitative form of selection bias is
defined and studied. Based on the form of bias, three reproducibility probability-based approaches are proposed to estimate the true proportion of
positive studies in the structural and unstructured data. The reproducibility probability-based approach provides effective bias ad.. Read More»
DOI:
10.37421/2155-6180.2023.14.150
Journal of Biometrics & Biostatistics received 3254 citations as per Google Scholar report