. Author manuscript; available in PMC: 2017 Oct 1.

Published in final edited form as: Cancer Causes Control. 2016 Aug 12;27(10):1175–1185. doi: 10.1007/s10552-016-0795-5

Table 4.

Net clinical benefit of the novel model versus USMSTF guidelines using model cut-points targeted to improve (a) sensitivity, and (b) specificity, applied to the validation dataset

Model cut-point strategy	Measure of clinical benefit	Validation dataset results
Model cut-point strategy	Measure of clinical benefit	USMSTF guidelines (%)	Predictive model (%)	Difference (95 % CI)^a
Improve sensitivity compared to USMSTF guidelines
Specified sensitivity within training data: 88 %	Sensitivity	74.0	88.9	14.9 (9.5, 20.2)^***
Resulting cut-point = 0.075	Specificity	42.1	27.7	−14.4 (−16.7, −11.9)^***
	Overuse	83.2	83.7	0.5 (−0.6,1.6)
	Underuse	8.9	6.0	−2.9 (−5.0, −0.9)^†
Improve specificity compared to USMSTF guidelines
Specified specificity within training data = 51 %	Sensitivity	74.0	75.8	1.8 (−4.2, 7.4)
Resulting cut-point = 0.101	Specificity	42.1	46.2	4.1 (2.0, 6.7)^***
	Overuse	83.2	81.8	−1.4 (−2.7, −0.1)^†
	Underuse	8.9	7.7	−1.2 (−3.0, 0.6)

Difference is the estimate using predictive model minus the estimate using US guidelines and 95 % CI is based on 1,000 bootstrapped samples

p value < 0.05;

^**

p value < 0.01;

^***

p value < 0.001;

^†

Statistically significant at alpha = 0.05 based on 95 % bootstrap CI