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A repeated measures concordance correlation coefficient.

Tonya S King1, Vernon M Chinchilli, Josep L Carrasco

  • 1Division of Biostatistics, Department of Health Evaluation Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033, USA. tking@psu.edu

Statistics in Medicine
|January 12, 2007
PubMed
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This study introduces a new coefficient to measure agreement between two continuous measurements when repeated observations are available. This method enhances reliability assessment in longitudinal studies and bioassays.

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Measurement Science

Background:

  • The concordance correlation coefficient (CCC) is standard for assessing agreement between two continuous measurements.
  • Existing CCC methods are limited when repeated measurements per subject/method are involved, common in longitudinal studies.
  • Assessing agreement with repeated measures is crucial for data reliability in clinical and bioassay research.

Purpose of the Study:

  • To propose a novel coefficient for measuring agreement between two continuous measurements under repeated observations.
  • To extend the application of agreement coefficients to complex data structures like those in longitudinal studies.
  • To provide a robust statistical tool for evaluating inter-rater or inter-method reliability with repeated data.

Main Methods:

Related Experiment Videos

  • Development of a new concordance correlation coefficient specifically designed for repeated measurement data.
  • Application of the proposed coefficient to real-world examples, including clinical trial data and bioassay measurements.
  • Statistical validation and illustration of the coefficient's utility in assessing measurement agreement.

Main Results:

  • The proposed coefficient effectively quantifies agreement in the presence of repeated measurements.
  • Illustrative examples demonstrate the practical application and interpretation of the new coefficient.
  • The methodology provides a more accurate assessment of agreement compared to standard methods when data are not independent.

Conclusions:

  • The newly proposed coefficient offers a valuable extension for assessing agreement with repeated continuous measurements.
  • This method improves the reliability analysis of data from longitudinal studies and bioassays.
  • The coefficient provides a statistically sound approach for evaluating measurement concordance in complex designs.