Fig. 3From: Assessment of predictive performance in incomplete data by combining internal validation and multiple imputationSimulation distribution of AUC estimates obtained by different strategies. Boxplots showing distribution of AUC estimates across the 250 simulated data sets in a setting with moderate sample size (n=200), p=10 covariates, moderate missing at random (MAR) missingness (miss=25 % of values missing), balanced outcome class distribution (frac=0.5) and uncorrelated covariates (ρ=0) in the absence (theoretical auc=0.5; a) and presence (theoretical auc=0.66, see text; b) of a moderate true effect of the covariates on the outcome. The horizontal line denotes ‘true’ AUC related to a complete data set of size 200 (which is not necessarily equal to theoretical auc; see text). BS, bootstrap; CVK, K-fold CV; CVKrep, repeated K-fold CV; MI, multiple imputation; MI(-y), multiple imputation without including the outcome; No val., no validation (i.e., apparent performance); OOB, out-of-bag estimate; opt.corr., ordinary optimism-corrected estimate; SS, subsampling; Val, validationBack to article page