Table 3.

Simulation results for the settings with p = 10 and t = 20. Cox: censoring weights are estimated from a Cox PH model. Lasso-Cox1: censoring weights are estimated from a Cox PH model using the selected variables based on a Lasso Cox PH model. Lasso-Cox2: censoring weights are estimated directly from a Lasso Cox PH model.

ζ	n	ℳ2	$\mathrm{\Delta }{\widehat{c}}_s^1(SD)$	$\mathrm{\Delta }{\widehat{c}}_s^2(SD)$	$\mathrm{\Delta }{\widehat{c}}_g^1(SD)$	$\mathrm{\Delta }{\widehat{c}}_g^2(SD)$	ctrue
0.3	50	Cox	0.088(0.074)	0.021(0.046)	0.055(0.095)	0.002(0.056)	0.858
		Lasso-Cox1			0.051(0.093)	0.002(0.054)
		Lasso-Cox2			0.102(0.082)	0.029(0.050)
	100	Cox	0.082(0.046)	0.024(0.029)	0.034(0.064)	0.005(0.035)
		Lasso-Cox1			0.029(0.062)	0.006(0.032)
		Lasso-Cox2			0.097(0.052)	0.032(0.033)
	500	Cox	0.077(0.020)	0.027(0.013)	0.016(0.031)	0.006(0.015)
		Lasso-Cox1			0.013(0.031)	0.008(0.014)
		Lasso-Cox2			0.092(0.022)	0.036(0.014)
0.8	50	Cox	0.026(0.034)	0.005(0.029)	0.013(0.036)	0.002(0.029)	0.846
		Lasso-Cox1			0.009(0.033)	0.002(0.028)
		Lasso-Cox2			0.040(0.048)	0.010(0.049)
	100	Cox	0.024(0.023)	0.007(0.021)	0.004(0.022)	0.000(0.019)
		Lasso-Cox1			0.004(0.023)	0.001(0.020)
		Lasso-Cox2			0.036(0.028)	0.011(0.036)
	500	Cox	0.022(0.010)	0.008(0.009)	0.002(0.010)	0.000(0.008)
		Lasso-Cox1			0.003(0.010)	0.001(0.008)
		Lasso-Cox2			0.033(0.011)	0.013(0.009)

Note that $\mathrm{\Delta }{\widehat{c}}_s^1(SD)$ and $\mathrm{\Delta }{\widehat{c}}_s^2(SD)$ are the same for Cox, Lasso-Cox1 and Lasso-Cox2 becasue they do not require the estimation of censoring weights.