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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Matrix-based concordance correlation coefficient for repeated measures.

Sasiprapa Hiriote1, Vernon M Chinchilli

  • 1Department of Statistics, Faculty of Science, Silpakorn University, Nakorn Pathom, 73000, Thailand. ssiprapa@su.ac.th

Biometrics
|February 11, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new measure, the matrix-based concordance correlation coefficient (MCCC), for assessing agreement with repeated measurements. The MCCC extends Lin's CCC and performs well in simulations for complex data analysis.

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Area of Science:

  • Biostatistics
  • Statistical Methods
  • Clinical Research

Background:

  • Lin's concordance correlation coefficient (CCC) is widely used for agreement assessment in clinical studies.
  • Existing methods are limited for situations with repeated measurements by raters or methods.

Purpose of the Study:

  • To propose a novel matrix-based concordance correlation coefficient (MCCC) for handling repeated measures.
  • To develop and validate a U-statistic-based estimator for the MCCC.

Main Methods:

  • Developed a matrix norm-based MCCC for agreement between p x 1 vectors of random variables.
  • Proposed a U-statistic estimator for MCCC and derived its asymptotic normal distribution.
  • Conducted simulation studies to evaluate the estimator's performance.

Main Results:

  • The MCCC generalizes Lin's CCC for p=1.
  • The proposed U-statistic estimator demonstrates good accuracy, precision, and coverage probability, especially for sample sizes n > 40.
  • The estimator's asymptotic distribution is proven to be normal.

Conclusions:

  • The matrix-based concordance correlation coefficient (MCCC) is a robust tool for agreement assessment with repeated measurements.
  • The proposed U-statistic estimator is reliable for practical applications in biostatistics and clinical research.
  • Demonstrated MCCC application using real data from asthma and women's health studies.