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Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers

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Adjusted inference procedures for the interobserver agreement in twin studies.

Stephanie N Dixon¹, Allan Donner², Mohamed M Shoukri³

¹Department of Epidemiology and Biostatistics, Western University, London, ON, Canada Division of Nephrology, Department of Medicine, London Health Science Centre and Western University, London, ON, Canada Stephanie.dixon@lhsc.on.ca.

Statistical Methods in Medical Research

|March 15, 2013

Summary

We developed new statistical methods to accurately assess agreement between two raters for clustered data, like twins. Our adjusted procedures, including confidence intervals and sample size estimation, offer valid inferences for clustered agreement analysis.

Keywords:

Paired data confidence interval construction goodness-of-fit significance test kappa statistic sample size estimation

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Area of Science:

Statistics
Biostatistics
Medical Research Methodology

Background:

Evaluating inter-rater reliability is crucial in research, especially with clustered data.
Traditional agreement measures may be inadequate for clustered data (e.g., twins, paired organs).
Accurate statistical inference is needed for clustered agreement assessments.

Purpose of the Study:

To propose adjusted statistical inference procedures for assessing agreement between two raters in clustered settings.
To develop methods for confidence intervals, hypothesis testing, and sample size estimation for clustered agreement.
To provide valid and reliable tools for analyzing clustered reliability data.

Main Methods:

Development of adjusted inference procedures for the kappa statistic in clustered settings.
Construction of confidence intervals and a significance test for kappa.
Derivation of a sample size estimation formula incorporating clustered effects.
Simulation studies to evaluate the performance of the proposed methods.
Application of methods to a real-world literature example.

Main Results:

The proposed adjusted inference procedures provide valid results for clustered agreement.
A simple adjustment using an estimated design effect ensures accurate inferences.
The developed methods effectively handle the dependencies within clusters.
Simulation results support the validity and utility of the proposed techniques.

Conclusions:

The adjusted inference procedures are effective for evaluating two-rater agreement in clustered data.
The methods offer a statistically sound approach for confidence intervals, significance testing, and sample size calculations.
These procedures enhance the reliability of agreement assessments in studies involving twins or paired measurements.