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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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Analytical methods for correlated data arising from multicenter hearing studies.

Yanghui Sheng¹, Ce Yang², Sharon Curhan^3,4

¹Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Statistics in Medicine

|September 20, 2022

Summary

This summary is machine-generated.

This study introduces a linear mixed-effects model to accurately analyze hearing loss data. The proposed method accounts for complex correlations, offering more reliable estimates than traditional single-ear models, especially when dealing with confounding factors.

Keywords:

audiometry both ears clustered data hearing data analysis mixed-effects model multi-level cluster single-ear method

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Area of Science:

Epidemiology
Biostatistics
Audiology

Background:

Estimating hearing loss associations with exposures is complex due to clustered data.
Traditional methods often overlook multilevel correlations from ears, testing sites, and audiologists.

Purpose of the Study:

To propose and evaluate a linear mixed-effects model for epidemiological hearing studies.
To compare the performance of the proposed model against commonly used single-ear regression models.

Main Methods:

Development of a linear mixed-effects model accounting for multilevel data structures.
Comparison of single-ear linear regression with both-ears linear mixed models.
Theoretical analysis and simulation studies.

Main Results:

Single-ear models can be less efficient or biased with participant or ear-level confounders.
The proposed both-ears model provides unbiased and more efficient estimators.
Adjusting for testing sites and audiologists further improves the both-ears model's efficiency.

Conclusions:

Linear mixed-effects models offer superior accuracy for analyzing hearing loss data.
The both-ears approach is recommended for robust estimation of exposure-hearing loss associations.
Accurate statistical modeling is crucial for reliable epidemiological findings in hearing research.