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

Abstract

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. Retrospective cohort study of a national CI database (HERMES) and a nonoverlapping single-institution CI database. Multi-institutional (32 CI centers). Adult CI recipients (n = 4,046 patients). Mean absolute error (MAE) between imputed and observed speech perception scores. Imputation models of preoperative speech perception measures demonstrate a MAE of less than 10% for feature triplets of CNCw/AzBio in quiet/AzBio +10 (MICE: MAE, 9.52%; 95% confidence interval [CI], 9.40-9.64; KNN: MAE, 8.93%; 95% CI, 8.83-9.03) and AzBio in quiet/AzBio +5/AzBio +10 (MICE: MAE, 8.85%; 95% CI, 8.68-9.02; KNN: MAE, 8.95%; 95% CI, 8.74-9.16) with one feature missing. Postoperative imputation can be safely performed with up to four of six features missing in a set of CNCw and AzBio in quiet at 3, 6, and 12 months postcochlear implantation using MICE (MAE, 9.69%; 95% CI, 9.63-9.76). For multivariable analysis of CI performance prediction, imputation increased sample size by 72%, from 2,756 to 4,739, with marginal change in adjusted R2 (0.13 raw, 0.14 imputed). Missing data across certain sets of common speech perception tests may be safely imputed, enabling multivariate analysis of one of the largest CI outcomes data sets to date.