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. 2020 Mar 10;80(5):932–954. doi: 10.1177/0013164420911136

Table 2.

Average Bias, RMSE, and Correlation Values for NMAR.

Method Test length Missing proportion (%) Bias RMSE Correlation
FIML 20 30 0.000 0.348 .911
MICE-RFI2 20 30 0.000 0.371 .898
MICE-CART 20 30 0.000 0.373 .897
MICE-CART2 20 30 0.000 0.375 .896
MICE-RFI 20 30 0.000 0.379 .893
Zero replacement 20 30 0.000 0.466 .834
FIML 40 30 0.000 0.291 .947
MICE-CART 40 30 0.000 0.312 .939
MICE-RFI2 40 30 0.000 0.318 .937
MICE-CART2 40 30 0.000 0.319 .936
MICE-RFI 40 30 0.000 0.320 .936
Zero replacement 40 30 0.000 0.395 .901
FIML 60 30 0.000 0.254 .962
MICE-CART 60 30 0.000 0.270 .957
MICE-CART2 60 30 0.000 0.281 .954
MICE-RFI2 60 30 0.000 0.281 .954
MICE-RFI 60 30 0.000 0.282 .954
Zero replacement 60 30 0.000 0.350 .928
FIML 20 40 0.000 0.433 .860
MICE-CART2 20 40 0.000 0.456 .842
MICE-RFI2 20 40 0.000 0.460 .839
MICE-CART 20 40 0.000 0.463 .839
MICE-RFI 20 40 0.000 0.473 .827
Zero replacement 20 40 0.000 0.503 .804
FIML 40 40 0.000 0.370 .914
MICE-CART 40 40 0.000 0.386 .905
MICE-CART2 40 40 0.000 0.393 .901
MICE-RFI2 40 40 0.000 0.401 .898
MICE-RFI 40 40 0.000 0.409 .893
Zero replacement 40 40 0.000 0.434 .879
FIML 60 40 0.000 0.331 .936
MICE-CART 60 40 0.000 0.346 .929
MICE-CART2 60 40 0.000 0.352 .926
MICE-RFI2 60 40 0.000 0.364 .922
MICE-RFI 60 40 0.000 0.370 .918
Zero replacement 60 40 0.000 0.386 .911
FIML 20 70 0.000 0.680 .618
MICE-CART 20 70 −0.004 0.708 .585
MICE-RFI2 20 70 0.000 0.733 .491
MICE-RFI 20 70 0.000 0.738 .487
MICE-CART2 20 70 0.000 0.754 .485
Zero replacement 20 70 0.000 0.777 .463
FIML 40 70 0.000 0.635 .728
MICE-CART 40 70 −0.010 0.652 .708
MICE-RFI 40 70 −0.001 0.683 .660
Zero replacement 40 70 0.000 0.687 .665
MICE-RFI2 40 70 −0.001 0.706 .634
MICE-CART2 40 70 0.001 0.727 .606
FIML 60 70 0.001 0.595 .779
MICE-CART 60 70 −0.011 0.601 .770
Zero replacement 60 70 0.000 0.635 .739
MICE-RFI 60 70 −0.001 0.643 .727
MICE-RFI2 60 70 −0.001 0.676 .698
MICE-CART2 60 70 0.001 0.682 .683

Note. RMSE = root mean square error; NMAR = not missing at random; FIML = full-information maximum likelihood; MICE-CART = multiple imputation with chain equations utilizing classification and regression trees; MICE-RFI = multiple imputation with chain equations utilizing random forest imputation.