Rationale for Using Multiple Regression Analysis with Complex Interferences

Abstract

Non-specificities and interferences may become complex when they involve the analyte as well as other interfering substances. These non-specificities and interferences are known as analyte-dependent and multi-interferent interferences. Multiple regression analysis has proven valuable in analysing this type of interference, but the theoretical foundation for using multiple regression analysis to study the basic mechanisms of interference has not been explicitly demonstrated.Graph theory can depict and model the basic mechanisms of interferences and the possible interactions. The relationship between the analyte, the interferents, and the response of the instrument to these entities can be approximated by a polymial of order three, which includes partial derivatives and cross-terms. The partial derivatives relate to the different interactions found with the graph theory model. Further, the partial derivatives can be associated with the coefficients in the multiple regression analysis when the respective values of the three variables (analyte, interferent one, and interferent two) are multiplied by one another. One can decide to retain or discard the coefficient of a variable, based on the statistical significance of the coefficient. The respective interactions in the graphic model can then be assembled and the framework of the interference mechanism established.

Bi-variate approach to negative interference of bilirubin towards an acetaminophen assay

“…The conventional method of using linear regression in modeling interferences may not be reliable in most circumstances [5]. As demonstrated in this study, interference of acetaminophen assay by bilirubin is variable, and dependent on both acetaminophen and bilirubin concentrations.…”

Factors Impacting Liquidity of Banks: An Empirical Study From the Banking Sector in the Uae

“…The independent variables can be either dichotomous or continuous. A multiple regression analysis provides information about the interaction between dependent and independent variables, or between several independent variables, which can then be used to enable further manipulation (Kroll & Chesler, 1992). Through regression, the determination of the statistical significance of a coefficient is possible.…”

Determination of cholesterol in blood. Part 2

“…The use of the samples under consideration was accompanied by the presence of the relative systematic error of measurements of the total cholesterol concentration in serum up to 0.7% (Procedure 1), 1.7% (Procedure 2), 9.4% (Procedure 3), 14.3% (Procedure 4), and 5.7% (Procedure 5) [140].…”