Statistical Considerations in Assay Validation.

Unlike the statistical and experimental methods for assay development, which are not well defined, the statistical methods for assay validation and data analysis have been established and widely published.

Assay Performance Metrics. As part of its Harmonized Tripartite Guideline, the International Conference on Harmonization (ICH; Geneva) released the following two documents: “Text on Validation of Analytical Procedures” and “Validation of Analytical Procedures: Methodology.”1,2 These documents provide detailed definitions of appropriate validation parameters and suitable evaluation methods. These documents also provide the metrics of assay performance and their definitions (see Table II).

When reviewing the statistical considerations for assay validation, the standard validation parameters should be grouped into two areas: the measurements themselves and the variability in those measurements (see Table II). The validation metrics related to an analytical assay’s measurements are accuracy, linearity, and specificity. The validation metrics related to the variability of an analytical assay are precision, robustness, range, limit of quantitation, and limit of detection.The CLSI guidelines provide a solid methodology for validating an assay against the chosen parameters.7 Although specialized software is available for analyzing the data from assay validation experiments, in accordance with CLSI methods, such data analysis can also be performed by using standard elements in Excel and any statistical software package.7 The most common statistical methods used in assay validation are precision analysis, linearity analysis, and methods comparison.

1 comment:

Fiverr Work said...

The validation metrics related to an analytical assay’s measurements are accuracy, linearity, and specificity. The validation metrics related to the variability of an analytical assay are precision, robustness, range, limit of quantitation, and limit of detection. fengshui