Table 1. Prediction accuracy (with 95% confidence interval) and sensitivity with respect to selected risk score thresholds that ensure ~80% specificity by Cox models using a single inferred AD-NC trait.

Values in this table are reflecting the Cox risk model prediction performance at one point in the ROC curves as shown in Fig 3, with corresponding risk score thresholds. Samples with predicted risk scores greater than the selected threshold were considered as Predicted Positives (incident ADD), otherwise Predicted Negatives (not developing ADD).

		Amyloid-β		Tangles		Global AD Pathology		Pathologic AD
		Y3	Y5	Y3	Y5	Y3	Y5	Y3	Y5
NCI/MCI -> ADD	Accuracya (95% CI)	0.798 (0.773, 0.822)	0.788 (0.763, 0.813)	0.802 (0.777, 0.825)	0.795 (0.771, 0.818)	0.801 (0.775, 0.823)	0.796 (0.771, 0.819)	0.809 (0.784, 0.831)	0.803 (0.778, 0.826)
	Sensitivity b	0.772	0.699	0.822	0.756	0.797	0.764	0.911	0.829
	Specificity c	0.800	0.800	0.800	0.800	0.800	0.800	0.800	0.800
NCI -> ADD	Accuracya (95% CI)	0.796 (0.768, 0.823)	0.792 (0.763, 0.7819)	0.794 (0.765, 0.821)	0.791 (0.761, 0.818)	0.795 (0.767, 0.823)	0.793 (0.764, 0.820)	0.804 (0.775, 0.830)	0.805 (0.777, 0.832)
	Sensitivity b	0.650	0.634	0.550	0.609	0.600	0.658	0.950	0.902
	Specificity c	0.800	0.800	0.800	0.800	0.800	0.800	0.800	0.800

^a.Accuracy= (# True Positive Predictions + # True Negative Predictions) / (# of test samples)

^b.Sensitivity = (# True Positive Predictions) / (# Positives in test samples) = True Positive Fraction

^c.Specificity = (# True Negative Predictions) / (# Negatives in test samples) = 1−False Positive Fraction