Evaluation of Different Estimation Methods for Accuracy and Precision in Biological Assay Validation

Biological assays (bioassays) are procedures to estimate the potency of a substance by studying its effects on living organisms, tissues, and cells. Bioassays are essential tools for gaining insight into biologic systems and processes including, for example, the development of new drugs and monitoring environmental pollutants. Two of the most important parameters of bioassay performance are relative accuracy (bias) and precision. Although general strategies and formulas are provided in USP<1033>, a comprehensive understanding of the definitions of bias and precision remain elusive. Additionally, whether there is a beneficial use of data transformation in estimating intermediate precision remains unclear. Finally, there are various statistical estimation methods available that often pose a dilemma for the analyst who must choose the most appropriate method. To address these issues, we provide both a rigorous definition of bias and precision as well as three alternative methods for calculating relative standard deviation (RSD). All methods perform similarly when the RSD ≤10%. However, the USP estimates result in larger bias and root-mean-square error (RMSE) compared to the three proposed methods when the actual variation was large. Therefore, the USP method should not be used for routine analysis. For data with moderate skewness and deviation from normality, the estimates based on the original scale perform well. The original scale method is preferred, and the method based on log-transformation may be used for noticeably skewed data.LAY ABSTRACT: Biological assays, or bioassays, are essential in the development and manufacture of biopharmaceutical products for potency testing and quality monitoring. Two important parameters of assay performance are relative accuracy (bias) and precision. The definitions of bias and precision in USP 〈1033〉 are elusive and confusing. Another complicating issue is whether log-transformation should be used for calculating the intermediate precision. It is often challenging for analysts to pick the most appropriate estimation method. To address these issues, we give a rigorous definition of bias and precision and provide three alternative methods of calculating RSD. We demonstrate that the RSD formula in USP 〈1033〉 tends to have larger bias and root-mean-square error (RMSE) than the three proposed methods.