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OBJECTIVE: Assess the real-world performance of popular imputation algorithms on cochlear implant (CI) candidate audiometric data. METHODS: 7,451 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) 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.
Pavelchek et al. (Mon,) studied this question.