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. Author manuscript; available in PMC: 2017 Nov 1.
Published in final edited form as: Kidney Int. 2016 Sep 19;90(5):1090–1099. doi: 10.1016/j.kint.2016.07.018

Table 4.

Performance of the multivariable Cox regression models for CKD

MODEL 1 MODEL 2 MODEL 3 MODEL 4
Performance measure Clinical Clinical + Classification MODEL 2 + SCysC MODEL 2 + pNGAL
OVERALL

(a) LR statistic 70.56 123.85 69.29
(b) explained variation (%) 20.0% 28.6% 30.1% 28.5%

DISCRIMINATION

C statistic 0.739 0.794 0.810 0.794

CALIBRATION

slope shrinkage estimate 0.965 0.956 0.937 0.952

ADDED VALUE (c1) (c2) (c3)

NRI events % (CI) 40.2 (25.8 – 54.7) 9.2 (−5.7 – 24.1) 12.4 (−2.3 – 27.1)
NRI non-events % (CI) 34.5 (24.8 – 44.1) 41.0 (31.3 – 50.7) −31.5 (−41.3 – −21.8)

IDI 0.10 (0.07 – 0.13) 0.009 (0.001 – 0.017) 0.001 (−0.001 – 0.002)
IDI events 0.07 (0.04 – 0.09) 0.005 (−0.002 – 0.013) 0.002 (0.000 – 0.003)
IDI non-events 0.03 (0.02 – 0.05) 0.004 (0.000 – 0.007) −0.001 (−0.002 – 0.000)
(a)

For all likelihood-ratio tests: MODEL 1 nested in MODEL 2, and MODEL 2 nested in MODEL 3 and MODEL 4, p<0.001 was obtained;

(b)

Nagelkerke R2;

(c1) corresponds to the added value to the clinical model attained by Classification, on the developed sample; (c2) and (c3) correspond, respectively, to the added value to the clinical + Classification model attained by SCysC and pNGAL, on the developed sample.