Journal
STATISTICS IN MEDICINE
Volume 40, Issue 9, Pages 2155-2176Publisher
WILEY
DOI: 10.1002/sim.8895
Keywords
assessment of agreement; Bland– Altman method; limits of agreement; quantile regression
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The paper introduces a new method of constructing an assessment set based on differences to more comprehensively evaluate the agreement between measurement variables, and proposes two different approaches based on parameters and non-parameters to adapt to different measurement distribution scenarios.
The Bland-Altman method, which assesses agreement via an assessment set constructed by the difference of the measurement variables, has received great attention. Other assessment approaches have been proposed following the same difference-based framework. However, the exact assessment set constructed by the difference is achievable only for measurements with certain joint distributions. To provide a more general assessment framework, we propose two approaches. First, when the measurement distribution is known, we propose a parametric approach that constructs the assessment set through a measure of closeness corresponding to the distribution. Second, when the measurement distribution is unknown, we propose a nonparametric approach that constructs the assessment set through quantile regression. Both approaches quantify the degree of agreement with the presence of both systematic and random measurement errors, and enable one to go beyond the difference-based approach. Results of simulation and data analyses are presented to compare the two approaches.
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