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A robust permutation test for the concordance correlation coefficient.

Alan D Hutson1, Han Yu1

  • 1Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.

Pharmaceutical Statistics
|February 18, 2021
PubMed
Summary
This summary is machine-generated.

We developed a robust permutation test for concordance correlation coefficient (ρc) to assess agreement between paired variables. This new statistical method ensures accurate hypothesis testing, even with small sample sizes.

Keywords:
measures of agreementnon-normalsmall samplestudentization

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Area of Science:

  • Statistics
  • Biostatistics
  • Medical Data Analysis

Background:

  • Concordance correlation coefficient (ρc) is crucial for assessing agreement between paired measurements.
  • Existing methods for testing ρc may lack robustness, especially in certain conditions.

Purpose of the Study:

  • To develop and validate a robust permutation test for the concordance correlation coefficient (ρc).
  • To test the hypothesis H0: ρc = ρc(0) in a general setting.

Main Methods:

  • Development of a studentized statistic-based permutation test.
  • Asymptotic validity proof for uncorrelated but dependent paired variables.
  • Simulation studies across various distributional assumptions and sample sizes.

Main Results:

  • The proposed permutation test demonstrates robust type I error control.
  • The test maintains accuracy even with small sample sizes.
  • Asymptotic validity confirmed in theoretical analysis.

Conclusions:

  • The developed permutation test offers a reliable method for assessing concordance correlation.
  • The test is applicable in diverse settings, including medical measurements like cardiac output and echocardiography.