Detection of analytical bias when comparing two or more measuring methods

Abstract

AbstractThe main goal of this paper is to consider maximum likelihood inference for models used in the detection of analytical bias in the comparison of two or more methods of measurement. We embrace a functional errors‐in‐variables regression model with an EM‐type algorithm for computing maximum likelihood estimates and to obtain consistent estimators for the asymptotic variance of the maximum likelihood estimators, which seems not to be found in the literature. Wald‐type statistics are proposed for testing hypotheses related to the bias of the analytical methods with the asymptotic chi‐square distribution which guarantees correct asymptotic significance levels. Some approaches specific for the two‐methods comparison problem are not directly extendable to this more general situation. Results of simulation studies and an application to a real data set are also reported. Copyright © 2005 John Wiley & Sons, Ltd.