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 3496 citations as per Google Scholar report