Dataset Information

Both-Ear Method for the Analysis of Audiometric Data.

ABSTRACT:

Objective

Single-ear hearing measurements, such as better-ear, worse-ear or left/right ear, are often used as outcomes in auditory research, yet, measurements in the two ears of the same individual are often strongly but not perfectly correlated. We propose a both-ear method using the Generalized Estimating Equation approach for analysis of correlated binary ear data to evaluate determinants of ear-specific outcomes that includes information from both ears of the same individual.

Design

We first theoretically evaluated bias in odds ratio (OR) estimates based on worse-ear and better-ear hearing outcomes. A simulation study was conducted to compare the finite sample performances of single-ear and both-ear methods in logistic regression models. As an illustrative example, the single-ear and both-ear methods were applied to estimate the association of Dietary Approaches to Stop Hypertension adherence scores with hearing threshold elevation among 3135 women, aged 48 to 68 years, in the Nurses' Health Study II.

Results

Based on statistical theories, the worse-ear and better-ear methods could bias the OR estimates. The simulation results led to the same conclusion. In addition, the simulation results showed that the both-ear method had satisfactory finite sample performance and was more efficient than the single-ear method. In the illustrative example, the confidence intervals of the estimated ORs for the association of Dietary Approaches to Stop Hypertension scores and hearing threshold elevation using the both-ear method were narrower, indicating greater precision, than for those obtained using the other methods.

Conclusions

The worse-ear and better-ear methods may lead to biased estimates, and the left/right ear method typically results in less-efficient estimates. In certain settings, the both-ear method using the Generalized Estimating Equation approach for analyses of audiometric data may be preferable to the single-ear methods.

SUBMITTER: Chen C

PROVIDER: S-EPMC9398921 | biostudies-literature | 2022 Sep-Oct 01

REPOSITORIES: biostudies-literature

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Publications

Both-Ear Method for the Analysis of Audiometric Data.

Chen Chen C Zhang Ning N Curhan Gary C GC Curhan Sharon G SG Wang Molin M

Ear and hearing 20220316 5

<h4>Objective</h4>Single-ear hearing measurements, such as better-ear, worse-ear or left/right ear, are often used as outcomes in auditory research, yet, measurements in the two ears of the same individual are often strongly but not perfectly correlated. We propose a both-ear method using the Generalized Estimating Equation approach for analysis of correlated binary ear data to evaluate determinants of ear-specific outcomes that includes information from both ears of the same individual.<h4>Desi ...[more]

PMID: 35302530

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Project description:ObjectiveAssess the real-world performance of popular imputation algorithms on cochlear implant (CI) candidate audiometric data.Methods7,451 audiograms from patients undergoing CI candidacy evaluation were pooled from 32 institutions with complete case analysis yielding 1,304 audiograms. Imputation model performance was assessed with nested cross-validation on randomly generated sparse datasets with various amounts of missing data, distributions of sparsity, and dataset sizes. A threshold for safe imputation was defined as root mean square error (RMSE) <10dB. Models included univariate imputation, interpolation, multiple imputation by chained equations (MICE), k-nearest neighbors, gradient boosted trees, and neural networks.ResultsGreater quantities of missing data were associated with worse performance. Sparsity in audiometric data is not uniformly distributed, as inter-octave frequencies are less commonly tested. With 3-8 missing features per instance, a real-world sparsity distribution was associated with significantly better performance compared to other sparsity distributions (Δ RMSE 0.3 dB- 5.8 dB, non-overlapping 99% confidence intervals). With a real-world sparsity distribution, models were able to safely impute up to 6 missing datapoints in an 11-frequency audiogram. MICE consistently outperformed other models across all metrics and sparsity distributions (p < 0.01, Wilcoxon rank sum test). With sparsity capped at 6 missing features per audiogram but otherwise equivalent to the raw dataset, MICE imputed with RMSE of 7.83 dB [95% CI 7.81-7.86]. Imputing up to 6 missing features captures 99.3% of the audiograms in our dataset, allowing for a 5.7-fold increase in dataset size (1,304 to 7,399 audiograms) as compared with complete case analysis.ConclusionPrecision medicine will inevitably play an integral role in the future of hearing healthcare. These methods are data dependent, and rigorously validated imputation models are a key tool for maximizing datasets. Using the largest CI audiogram dataset to-date, we demonstrate that in a real-world scenario MICE can safely impute missing data for the vast majority (>99%) of audiograms with RMSE well below a clinically significant threshold of 10dB. Evaluation across a range of dataset sizes and sparsity distributions suggests a high degree of generalizability to future applications.

Dataset Information

Both-Ear Method for the Analysis of Audiometric Data.

Objective

Design

Results

Conclusions

Publications

Both-Ear Method for the Analysis of Audiometric Data.

Similar Datasets

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