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A short note on jackknifing the concordance correlation coefficient.

Dai Feng1, Richard Baumgartner, Vladimir Svetnik

  • 1Biometrics Research, Merck Research Lab., Rahway, NJ, U.S.A.

Statistics in Medicine
|August 2, 2013
PubMed
Summary
This summary is machine-generated.

This study establishes a sufficient condition for the validity of the jackknife method in constructing confidence intervals (CI) for Lin

Keywords:
U-statisticsconcordance correlation coefficientjackknife

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Area of Science:

  • Statistics
  • Applied Mathematics

Background:

  • Lin's concordance correlation coefficient (CCC) is widely used to measure agreement.
  • Confidence intervals (CI) for CCC estimates are crucial for assessing reliability.
  • Existing methods for CCC CI lack theoretical validation for jackknifing.

Purpose of the Study:

  • To provide a theoretical basis for the jackknife method in CCC CI construction.
  • To establish a sufficient condition for the validity of jackknife-based CCC CI.
  • To enhance the statistical rigor of agreement measurement analysis.

Main Methods:

  • Theoretical statistical analysis.
  • Mathematical derivation of conditions for jackknife validity.
  • Focus on the theoretical underpinnings of statistical inference.

Main Results:

  • A sufficient condition for the valid application of the jackknife method to CCC is established.
  • This theoretical proof supports the use of jackknifing for CCC CI.
  • The findings bridge a gap in the theoretical understanding of CCC analysis.

Conclusions:

  • The theoretical condition presented validates the use of jackknifing for CCC confidence intervals.
  • This work provides a rigorous foundation for applied statisticians using CCC.
  • Enhances confidence in statistical agreement measures through theoretical validation.