Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation

Table 1 Simulation settings

Parameter	Notation	Values
Predictive performance
Sample size	n	100, 200, 500, 1000
Number of covariates	p	1, 5, 10, 20
Correlation among covariates	ρ	0, 0.25
Outcome case frequency	frac	0.5, 0.25
Theoretical AUC	auc	0.5, 0.58, 0.66, 0.74, 0.82
Proportion of missing values among covariates	miss	0.125, 0.25, 0.375, 0.5, 0.625, 0.75
Missingness mechanism		MCAR, MAR, MARblock
Added predictive performance
		Baseline covariates	Additional covariates
Sample size	n	100, 200, 500, 1000
Number of covariates	p₀, p₁	1, 5, 10	1,5,10,20
Correlation among covariates	ρ₀, ρ₁	0	0, 0.25
Outcome case frequency	frac	0.5, 0.25
Theoretical (change in) AUC	auc₀, Δauc	0.6	0, 0.04, 0.08, 0.12, 0.16
Proportion of missing values among covariates	miss₀, miss₁	0, 0.5	0.125, 0.25, 0.375, 0.5, 0.625, 0.75
Missingness mechanism		MCAR, MAR, MARblock

AUC area under the receiver-operating characteristic (ROC) curve, MAR missing at random, MARblock blockwise missing at random, MCAR missing completely at random

ISSN: 1471-2288