Biological assays are essential tools in biomedical and pharmaceutical research. In simplest terms, such an assay is an analytical method used to measure or predict a response in a biological system in the presence of a given stimulus (e.g., drug). The inherent complexity involved in evaluating a biological system requires the use of rigorous and appropriate tools for data analysis. Linear and nonlinear regression models represent critically important statistical analyses used to define the relationships between variables of interest in biological systems. Recent challenges relating to the reproducibility of published data suggest the absence of standardized and routine use of statistics to support experimental results across a wide range of scientific disciplines. The current situation warrants an introductory review of basic regression concepts using current, practical examples, along with references to in-depth resources. The goal is to provide the necessary information to help standardize the analysis of biological assays in academic research and drug discovery and development, elevating their utility and increasing data transparency and reproducibility. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.
Keywords: EC50; Emax; Hill slope; IC50; Linear regression; Maximal effect; bioassay; biological assay; coefficient of determination (r2); confidence interval (CI); correlation; data integrity; dose-response; effective concentration; four parameter logistic (4PL); goodness of fit; interpolation; mechanism of action (MOA); nonlinear regression; ordinary least squares (OSL); p-value; quantitative; relative potency; reproducibility; residuals; standard curve; standard deviation (SD); standard error; statistics; sum of squares; transformation; unknown(s).
© 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.