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Related Experiment Video

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Power calculation for the likelihood ratio-test when comparing two dependent intraclass correlation coefficients.

B Giraudeau¹, R Porcher, J Y Mary

¹INSERM CIC 202, Faculté de Médecine, 10 Bd Tonnellé, BP 3223, 37032 Tours Cedex 1, France. giraudeau@med.univ-tours.fr

Computer Methods and Programs in Biomedicine

|January 18, 2005

Summary

This study introduces statistical methods for comparing the reproducibility of two devices using dependent intraclass correlation coefficients (ICCs). The developed macros aid in planning reproducibility studies by calculating the necessary number of subjects and replicates for power analysis.

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Area of Science:

Biostatistics
Medical Device Evaluation
Statistical Methodology

Background:

Reproducibility studies are crucial for evaluating the consistency of measurements from medical devices.
Comparing two devices requires assessing dependent intraclass correlation coefficients (ICCs) on a single subject sample.
Sample size and replicate number are key planning parameters for such studies.

Purpose of the Study:

To develop and propose statistical tools for power calculations in reproducibility studies comparing two devices.
To enable researchers to determine optimal sample sizes and replicate numbers for robust study design.

Main Methods:

Development of SAS and S-plus macros for power calculations.
Implementation of a simulation study to compare dependent ICCs.
Utilizing a likelihood ratio-test for comparing the dependent ICCs.

Main Results:

The proposed macros facilitate power calculations for comparing dependent ICCs.
Simulation results support the utility of the macros in study planning.
The likelihood ratio-test provides a method for comparing ICCs derived from dependent measurements.

Conclusions:

The developed macros offer a valuable resource for planning rigorous reproducibility studies.
Accurate power calculations ensure adequate sample sizes and replicates for reliable device comparison.
This methodology enhances the statistical foundation for medical device evaluation.