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. 2017 Oct 17;14(10):e1002410. doi: 10.1371/journal.pmed.1002410

Table 7. Comparison of discrimination and calibration for models fit by elastic net regularization versus traditional backwards selection.

Comparison (dataset) Elastic net regularization Traditional backwards selection
CVD model SAE model CVD model SAE model
Internal validation (SPRINT data)
Discrimination 0.71 (0.71/0.71) 0.71 (0.71/0.71) 0.70 (0.70/0.70) 0.71 (0.71/0.71)
Calibration slope 1.06 (1.06/1.06) 1.10 (1.10/1.10) 1.08 (1.08/1.08) 1.16 (1.16/1.16)
Calibration intercept −0.004 (−0.004/−0.004) −0.012 (−0.012/−0.012) −0.006 (−0.006/−0.006) −0.025 (−0.025/−0.025)
GND P value 0.68 (0.68/0.68) 0.12 (0.12/0.12) 0.79 (0.79/0.79) 0.24 (0.24/0.24)
External validation (ACCORD-BP data)
Discrimination 0.69 (0.69/0.69) 0.71 (0.71/0.71) 0.68 (0.68/0.68) 0.60 (0.60/0.60)
Calibration slope 0.96 (0.96/0.96) 1.01 (1.01/1.01) 1.04 (1.04/1.04) 0.54 (0.54/0.54)
Calibration intercept 0.006 (0.006/0.006) −0.003 (−0.003/−0.003) 0.002 (0.002/0.002) 0.064 (0.064/0.064)
GND P value 0.18 (0.18/0.18) 0.07 (0.07/0.07) 0.68 (0.68/0.68) <0.001 (<0.001/<0.001)

Values are given as overall (intervention arm/control arm).

CVD, cardiovascular disease; GND, Greenwood–Nam–D’Agostino test; SAE, severe adverse event.