Table 4.

Concordance, sensitivity, specificity, clinical net benefit, and overall AUCs.

Probability cutoff	Standard logistic regression				Robust logistic regression
	Concordance N (%)	Sensitivity	Specificity	Clinical net benefit	Concordance N (%)	Sensitivity	Specificity	Clinical net benefit
0.0	43 (33.1)	1.00	0.00	0.33	43 (33.1)	1.00	0.00	0.33
0.1	79 (60.8)	0.95	0.44	0.27	82 (63.1)	0.88	0.51	0.26
0.2	90 (69.2)	0.86	0.61	0.22	97 (74.6)	0.86	0.69	0.23
0.3	98 (75.4)	0.81	0.72	0.19	99 (76.2)	0.81	0.74	0.19
0.4	98 (75.4)	0.70	0.78	0.13	102 (78.5)	0.72	0.82	0.16
0.5	101 (77.7)	0.60	0.86	0.11	107 (82.3)	0.67	0.90	0.15
0.6	97 (74.6)	0.40	0.92	0.05	102 (78.5)	0.51	0.92	0.09
0.7	99 (76.2)	0.35	0.97	0.06	100 (76.9)	0.42	0.94	0.05
0.8	93 (71.5)	0.19	0.98	0.00	97 (74.6)	0.30	0.97	0.01
0.9	91 (70.0)	0.12	0.99	−0.03	93 (71.5)	0.19	0.98	−0.08
1.0	87 (66.9)	0.00	1.00	-	87 (66.9)	0.00	1.00	-
AUC	0.835				0.830

These characteristics rely on the age-genotype models for standard and robust logistic regression estimated based on leave-one-out cross-validation.