Abstract
In epidemiological hearing studies, estimating the association between exposures and hearing loss using audiometrically-assessed hearing measurements is challenging due to the complex correlation structure in the clustered data, with clusters formed by the two ears of the same individual and the testing site and audiologist. We propose a linear mixed-effects model to take into account the multilevel correlation structures of the data. Both theoretically and in simulation studies, we compare single-ear linear regression models commonly used in published hearing loss studies with the proposed both-ears linear mixed models properly accounting for the multi-level correlations. Our findings include (1) when there are only participant-level covariates, the worse-ear linear regression models produce unbiased but typically less efficient estimators than the both-ear and average-ear approaches; (2) when there are ear-level confounders, the worse-ear method may lead to biased estimators and the average-ear method produces unbiased but typically less efficient estimators than the both-ear method; (3) the both-ear method may gain efficiency when additionally adjusting for testing sites and audiologists. As an illustrative example, we applied the single-ear and both-ear methods to assess aspirin-hearing association in the Nurses' Health Study II.