title: Statistical method for determining and comparing limits of detection of bioassays.
abstract: The current bioassay development literature lacks the use of statistically robust methods for calculating the limit of detection of a given assay. Instead, researchers often employ simple methods that provide a rough estimate of the limit of detection, often without a measure of the confidence in the estimate. This scarcity of robust methods is likely due to a realistic preference for simple and accessible methods and to a lack of such methods that have reduced the concepts of limit of detection theory to practice for the specific application of bioassays. Here, we have developed a method for determining limits of detection for bioassays that is statistically robust and reduced to practice in a clear and accessible manner geared at researchers, not statisticians. This method utilizes a four-parameter logistic curve fit to translate signal intensity to analyte concentration, which is a curve that is commonly employed in quantitative bioassays. This method generates a 95% confidence interval of the limit of detection estimate to provide a measure of uncertainty and a means by which to compare the analytical sensitivities of different assays statistically. We have demonstrated this method using real data from the development of a paper-based influenza assay in our laboratory to illustrate the steps and features of the method. Using this method, assay developers can calculate statistically valid limits of detection and compare these values for different assays to determine when a change to the assay design results in a statistically significant improvement in analytical sensitivity.
authors.affiliation: Department of Bioengineering, University of Washington , Box 355061, Seattle, Washington 98195, United States.
authors.forename: Carly A
authors.initials: CA
authors.lastname: Holstein
authors.affiliation: Department of Statistics, University of Washington , Box 354322, Seattle, Washington 98195, United States.
authors.forename: Maryclare
authors.initials: M
authors.lastname: Griffin
authors.affiliation: Department of Statistics, University of Washington , Box 354322, Seattle, Washington 98195, United States.
authors.forename: Jing
authors.initials: J
authors.lastname: Hong
authors.affiliation: Department of Statistics, University of Washington , Box 354322, Seattle, Washington 98195, United States.
authors.forename: Paul D
authors.initials: PD
authors.lastname: Sampson
mesh_terms: D000465:Algorithms; D000818:Animals; D002138:Calibration; D002623:Chemistry Techniques, Analytical / Q000379:methods*; D019267:Hemagglutinin Glycoproteins, Influenza Virus / Q000032:analysis; D006801:Humans; D007251:Influenza, Human / Q000175:diagnosis; D057230:Limit of Detection; D016015:Logistic Models; D009975:Orthomyxoviridae / Q000302:isolation & purification; D009976:Orthomyxoviridae Infections / Q000175:diagnosis
chemical_list: D019267:Hemagglutinin Glycoproteins, Influenza Virus