Responsible Imputation of Missing Speech Perception Testing Data & Analysis of 4,739 Observations and Predictors of Performance

Abstract

Objective: To address outcome heterogeneity in cochlear implant (CI) research, we built imputation models using multiple imputation by chained equations (MICEs) and K-nearest neighbors (KNNs) to convert between four common open-set testing scenarios: Consonant-Nucleus-Consonant word (CNCw), Arizona Biomedical (AzBio) in quiet, AzBio +5, and AzBio +10. We then analyzed raw and imputed data sets to evaluate factors affecting CI outcome variability. Study Design: Retrospective cohort study of a national CI database (HERMES) and a nonoverlapping single-institution CI database. Setting: Multi-institutional (32 CI centers). Patients: Adult CI recipients (n = 4,046 patients). Main Outcome Measure(s): Mean absolute error (MAE) between imputed and observed speech perception scores. Results: Imputation models of preoperative speech perception measures demonstrate a MAE of less than 10% for feature triplets of CNCw/AzBio in quiet/AzBio +10