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Fiducial Confidence Intervals for Agreement Measures Among Raters Under a Generalized Linear Mixed Effects Model.

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Summary

This study introduces a generalized concordance correlation coefficient (CCC) for complex, multi-level data. The new method provides more accurate confidence intervals for assessing agreement in scientific measurements.

Keywords:
Fisher's Z‐transformationPoisson regressionfiducial quantityhierarchical designslongitudinal data

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Measurement Science

Background:

  • The classical concordance correlation coefficient (CCC) is widely used for assessing agreement.
  • Existing methods struggle with complex data structures like multi-level, multi-rater designs.
  • Need for robust methods to estimate agreement in intricate scientific measurements.

Purpose of the Study:

  • To generalize the concordance correlation coefficient (CCC) for three-level designs.
  • To develop a methodology for interval estimation of the generalized CCC.
  • To compare the proposed method with existing techniques like Fisher's Z-transformation.

Main Methods:

  • Developed a generalized CCC for multi-level, multi-rater data (discrete or continuous).
  • Employed a linearization technique and fiducial inference for interval estimation.
  • Applied the methodology to real-world data from clinical trials and neuroimaging.

Main Results:

  • The proposed fiducial inference approach yields confidence intervals with satisfactory coverage.
  • The new intervals are shorter and more efficient than those from Fisher's Z-transformation, even with moderate sample sizes.
  • Demonstrated applicability in osteoarthritis clinical trials and corticospinal tractography.

Conclusions:

  • The generalized CCC and its interval estimation provide a powerful tool for agreement assessment in complex designs.
  • The method offers improved precision and reliability over traditional approaches.
  • Highlights broad applicability in fields like artificial intelligence and medical research.