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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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A segmented measurement error model for modeling and analysis of method comparison data.

Lak N Kotinkaduwa1, Pankaj K Choudhary1

  • 1Department of Mathematical Sciences, FO 35, University of Texas at Dallas, Richardson, Texas, USA.

Statistics in Medicine
|August 5, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for comparing clinical measurement techniques that have a changing relationship. The approach accurately estimates agreement even when the relationship shifts across measurement ranges.

Keywords:
ECM algorithmagreementbootstrapchangepointconcordance correlationtotal deviation index

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Area of Science:

  • Biostatistics
  • Clinical Chemistry
  • Measurement Science

Background:

  • Method comparison studies are crucial for evaluating clinical measurement techniques.
  • Existing models often fail when the relationship between methods changes across the measurement range.
  • This limitation can lead to inaccurate assessments of agreement and similarity.

Purpose of the Study:

  • To develop a statistical methodology for analyzing method comparison studies with structural changes in the relationship.
  • To propose a segmented measurement error model accommodating piecewise linear relationships.
  • To address the challenge of an unknown changepoint in the method relationship.

Main Methods:

  • A segmented extension of the classical measurement error model was developed.
  • An expectation-maximization-type algorithm was used for model fitting.
  • Segment-specific measures of similarity and agreement were extended.
  • Inferences were performed using bootstrapping and maximum likelihood theory.

Main Results:

  • The proposed methodology accurately analyzes studies where measurement method relationships change.
  • The model effectively handles unknown changepoints in the relationship.
  • Simulation studies validated the methodology's performance.
  • The approach was successfully illustrated using digoxin assay data.

Conclusions:

  • The developed segmented measurement error model provides a robust framework for method comparison studies with changing relationships.
  • This methodology improves the accuracy of estimating similarity and agreement between clinical measurement methods.
  • The approach is applicable to various clinical assays exhibiting non-linear relationships.