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Due to the nature of pure-tone audiometry test, hearing loss data often has acomplicated correlation structure. Generalized estimating equation (GEE) iscommonly used to investigate the association between exposures and hearingloss, because it is robust to misspecification of the correlation matrix.However, this robustness typically entails a moderate loss of estimationefficiency in finite samples. This paper proposes to model the correlationcoefficients and use second-order generalized estimating equations to estimatethe correlation parameters. In simulation studies, we assessed the finitesample performance of our proposed method and compared it with other methods,such as GEE with independent, exchangeable and unstructured correlationstructures. Our method achieves an efficiency gain which is larger for thecoefficients of the covariates corresponding to the within-cluster variation(e.g., ear-level covariates) than the coefficients of cluster-level covariates.The efficiency gain is also more pronounced when the within-clustercorrelations are moderate to strong, or when comparing to GEE with anunstructured correlation structure. As a real-world example, we applied theproposed method to data from the Audiology Assessment Arm of the Conservationof Hearing Study, and studied the association between a dietary adherence scoreand hearing loss.

Generalized Estimating Equations for Hearing Loss Data with Specified Correlation Structures