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Table 1 Measures of predictive performance under different scenarios of missing data

From: Informative missingness in electronic health record systems: the curse of knowing

Scenario–method Mean prediction error (SD) RMSPE C-statistic Brier score Calibration-in-the-large
Reference
 No missing values − 0.009 (0.244) 0.244 0.663 0.209 0.016
Scenario 1
 Zero imputation − 0.004 (0.217) 0.217 0.699 0.197 0.019
 Mean imputation − 0.004 (0.217) 0.217 0.699 0.197 0.023
 CCA − 0.005 (0.244) 0.244 0.618 0.239 0.017
 Multiple imputation − 0.005 (0.269) 0.269 0.622 0.216 0.021
Scenario 2
 Zero imputation 0.104 (0.245) 0.266 0.663 0.220 − 0.467
 Mean imputation 0.104 (0.245) 0.266 0.663 0.220 − 0.467
 CCA 0.104 (0.245) 0.266 0.663 0.220 − 0.467
 Multiple imputation − 0.042 (0.246) 0.249 0.663 0.211 0.199
Scenario 3
 Zero imputation − 0.024 (0.292) 0.293 0.541 0.234 0.119
 Mean imputation − 0.040 (0.299) 0.302 0.541 0.239 0.210
 CCA − 0.104 (0.245) 0.266 0.662 0.220 − 0.461
 Multiple imputation − 0.043 (0.264) 0.268 0.663 0.212 0.207
Scenario 4
 Zero imputation − 0.151 (0.278) 0.316 0.500 0.248 0.782
  1. Abbreviations: CCA complete case analysis, SD standard deviation, RMPSE root mean squared prediction error. See main text for a description of the scenarios and details about the methods