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Related Experiment Videos

The intraclass correlation coefficient: distribution-free definition and test

D Commenges1, H Jacqmin

  • 1INSERM U330, Université de Bordeaux II, France.

Biometrics
|June 1, 1994
PubMed
Summary
This summary is machine-generated.

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This study introduces a generalized intraclass correlation coefficient derived from random effect models. It presents a score test for null intraclass correlation within the exponential family, applicable even with explanatory variables.

Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Modeling

Background:

  • The intraclass correlation coefficient (ICC) measures agreement within groups.
  • Existing ICC methods have limitations, especially for complex data structures.
  • Intracluster correlation is a related concept for clustered binary data.

Purpose of the Study:

  • To define a generalized intraclass correlation coefficient (ICC) using a broad class of random effect models.
  • To demonstrate that conventional ICC and intracluster correlation for binary data are special cases of this generalized coefficient.
  • To develop and validate a score test for the hypothesis of zero ICC within the exponential family of distributions.

Main Methods:

  • Development of a generalized intraclass correlation coefficient based on random effect models.

Related Experiment Videos

  • Derivation of a score test for null ICC within the exponential family.
  • Investigation of the test's properties, including its independence from specific distributions and relation to pairwise correlation.
  • Adaptation of the test to accommodate explanatory variables.

    • Main Results:

    • The generalized ICC encompasses conventional ICC and intracluster correlation for binary data.
    • A novel score test for null ICC in the exponential family was derived.
    • The derived test statistic is distribution-independent within the exponential family and linked to pairwise correlation.
    • The score test is adjustable for the inclusion of covariates.

    Conclusions:

    • The generalized ICC provides a unified framework for correlation estimation in random effect models.
    • The developed score test offers a robust method for assessing null ICC in the exponential family.
    • The test's flexibility allows for its application in various statistical contexts, including those with explanatory variables.