Journal of Biometrics & Biostatistics

ISSN: 2155-6180

Open Access

Yuankang Zhao

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

    Abstract HTML PDF

Google Scholar citation report
Citations: 3254

Journal of Biometrics & Biostatistics received 3254 citations as per Google Scholar report

Journal of Biometrics & Biostatistics peer review process verified at publons

Indexed In

arrow_upward arrow_upward