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A segmented measurement error model for modeling and analysis of method comparison data
Author(s) -
Kotinkaduwa Lak N.,
Choudhary Pankaj K.
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8677
Subject(s) - bootstrapping (finance) , estimator , computer science , range (aeronautics) , similarity (geometry) , observational error , piecewise , extension (predicate logic) , data mining , statistics , algorithm , econometrics , mathematics , artificial intelligence , mathematical analysis , materials science , image (mathematics) , composite material , programming language
Method comparison studies are concerned with estimating relationship between two clinical measurement methods. The methods often exhibit a structural change in the relationship over the measurement range. Ignoring this change would lead to an inaccurate estimate of the relationship. Motivated by a study of two digoxin assays where such a change occurs, this article develops a statistical methodology for appropriately analyzing such studies. Specifically, it proposes a segmented extension of the classical measurement error model to allow a piecewise linear relationship between the methods. The changepoint at which the transition takes place is treated as an unknown parameter in the model. An expectation‐maximization‐type algorithm is developed to fit the model and appropriate extensions of the existing measures are proposed for segment‐specific evaluation of similarity and agreement. Bootstrapping and large‐sample theory of maximum likelihood estimators are employed to perform the relevant inferences. The proposed methodology is evaluated by simulation and is illustrated by analyzing the digoxin data.