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Related Experiment Video

Updated: May 27, 2026

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity

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Published on: June 12, 2019

Assessing agreement with repeated measures for random observers.

Chia-Cheng Chen¹, Huiman X Barnhart

¹Section of Quantitative Health Sciences, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI 53226, USA. ccchen@mcw.edu

Statistics in Medicine

|November 19, 2011

Summary

This study introduces a new concordance correlation coefficient (CCC) for repeated measures, accounting for random observers and time points. This advances agreement analysis beyond traditional methods for quantitative measurements.

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Area of Science:

Statistics
Biostatistics
Quantitative Measurement Analysis

Background:

Agreement studies assess measurement consistency across observers.
The concordance correlation coefficient (CCC) is widely used for quantitative measurements.
Existing CCC methods are limited with repeated measures and random effects.

Purpose of the Study:

To propose a novel CCC for repeated measures data.
To address limitations of current CCC indices when observers and time points are random.
To provide a robust method for assessing inter-observer agreement in longitudinal studies.

Main Methods:

Development of a new CCC formula incorporating random observer and time point effects.
Conducting a simulation study to validate the proposed methodology.
Application of the new CCC to real-world vertebral body and image data.

Main Results:

The proposed CCC effectively handles repeated measures with random observers and time points.
Simulation results demonstrate the accuracy and reliability of the new method.
Illustrative examples show practical utility in diverse datasets.

Conclusions:

The new CCC offers a significant advancement for agreement studies with complex repeated measures.
This methodology enhances the assessment of observer agreement in scenarios with random effects.
The proposed CCC provides a valuable tool for researchers analyzing longitudinal quantitative data.