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• W4311600546 abstract "Method comparison studies mainly focus on determining if the two methods of measuring a continuous variable are agreeable enough to be used interchangeably. Typically, a standard mixed-effects model uses to model the method comparison data that assume normality for both random effects and errors. However, these assumptions are frequently violated in practice due to the skewness and heavy tails. In particular, the biases of the methods may vary with the extent of measurement. Thus, we propose a methodology for method comparison data to deal with these issues in the context of the measurement error model (MEM) that assumes a skew- <math xmlns=http://www.w3.org/1998/Math/MathML id=M1> <mi>t</mi> </math> (ST) distribution for the true covariates and centered Student’s <math xmlns=http://www.w3.org/1998/Math/MathML id=M2> <mi>t</mi> </math> (cT) distribution for the errors with known error variances, named STcT-MEM. An expectation conditional maximization (ECM) algorithm is used to compute the maximum likelihood (ML) estimates. The simulation study is performed to validate the proposed methodology. This methodology is illustrated by analyzing gold particle data and then compared with the standard measurement error model (SMEM). The likelihood ratio (LR) test is used to identify the most appropriate model among the above models. In addition, the total deviation index (TDI) and concordance correlation coefficient (CCC) were used to check the agreement between the methods. The findings suggest that our proposed framework for analyzing unreplicated method comparison data with asymmetry and heavy tails works effectively for modest and large samples." @default.
• W4311600546 created "2022-12-27" @default.
• W4311600546 creator A5033623018 @default.
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• W4311600546 date "2022-12-15" @default.
• W4311600546 modified "2023-10-14" @default.
• W4311600546 title "An Improved Measurement Error Model for Analyzing Unreplicated Method Comparison Data under Asymmetric Heavy-Tailed Distributions" @default.
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• W4311600546 doi "https://doi.org/10.1155/2022/3453912" @default.
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