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Table 2 Simulation results (30% censored; Scenario 2–Scenario 4)

From: A multiple imputation method based on weighted quantile regression models for longitudinal censored biomarker data with missing values at early visits

α α 0 α 1 α 2
Method Bias 100xRE Bias 100xRE Bias 100xRE
Scenario 2: MVN, Unstructured covariance  
OMNI 0.0017 -0.0006 -0.0013
CC-DL/2 0.0527 16.527 -0.5413 7.541 0.1161 28.516
MI 1 0.1809 8.011 -0.3166 11.718 0.0028 18.080
MI 2 0.0183 42.410 -0.0014 21.517 -0.0035 52.201
MI-CQR 0.0433 63.613 -0.0050 36.069 -0.0089 69.551
MI-wCQR 1 0.0319 71.852 -0.0099 42.007 -0.0031 78.523
MI-wCQR 2 0.0204 71.104 -0.0299 40.525 0.0031 77.691
MI-wCQR 3 0.0172 63.936 -0.0168 37.197 0.0030 70.009
Scenario 3: MVE, Exchangeable covariance  
OMNI 0.0028 0.0000 -0.0014
CC-DL/2 0.2094 12.616 -0.4212 7.660 0.0057 22.869
MI 1 0.4430 3.682 -0.6098 3.640 -0.0981 10.591
MI 2 0.0423 32.878 0.0008 11.074 -0.0088 44.840
MI-CQR 0.0667 58.224 -0.0183 36.201 -0.0128 71.372
MI-wCQR 1 0.0501 62.644 -0.0163 41.087 -0.0055 77.240
MI-wCQR 2 0.0379 62.214 -0.0256 40.690 0.0000 77.128
MI-wCQR 3 0.0273 59.215 -0.0041 37.352 0.0018 71.902
Scenario 4: MVE, Heteroscedastic covariance  
OMNI 0.0028 0.0000 -0.0014
CC-DL/2 0.1739 14.389 -0.4081 8.210 0.0171 23.356
MI 1 0.1892 3.537 -0.3084 1.531 0.0002 10.432
MI 2 0.0410 32.650 0.0011 11.025 -0.0083 44.126
MI-CQR 0.0649 58.870 -0.0141 36.739 -0.0125 71.618
MI-wCQR 1 0.0492 63.492 -0.0121 41.733 -0.0054 78.362
MI-wCQR 2 0.0340 63.222 -0.0223 40.015 0.0009 78.325
MI-wCQR 3 0.0273 59.195 -0.0041 37.005 0.0018 71.929
  1. OMNI: Omniscient; CC-DL/2: CC with censored values imputed by DL/2; MI-MCMC 1: MI-MCMC imputing only missing values; MI-MCMC 2: MI-MCMC imputing both censored and missing values; MI-CQR: MI-unweighted CQR; MI-wCQR 1: MI-weighted CQR using original probability of missing; MI-wCQR 2: MI-weighted CQR using estimated probability from censored values imputed by DL/2; MI-wCQR 3MI-weighted CQR using estimated probability from uncensored values only; RE: Relative Efficiency