Table 4.

Simulation results for settings with p = 100 and t = 20. Z has an exchangeable correlation structure with ρ. 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; Elastic-Net-Cox1: censoring weights are estimated from a Cox PH model using the selected variables based on an Elastic-net Cox PH model; Elastic-Net-Cox2: censoring weights are estimated directly from an Elastic-net Cox PH model.

ρ	ζ	n	ℳ2	$\mathrm{\Delta }{\widehat{c}}_g^1(SD)$	$\mathrm{\Delta }{\widehat{c}}_g^2(SD)$	ctrue
0	0.3	50	Lasso-Cox1	0.059(0.092)	0.003(0.062)	0.860
			Lasso-Cox2	0.107(0.085)	0.024(0.053)
		100	Lasso-Cox1	0.037(0.064)	0.003(0.041)
			Lasso-Cox2	0.099(0.054)	0.031(0.034)
	0.8	50	Lasso-Cox1	0.014(0.035)	0.002(0.040)	0.847
			Lasso-Cox2	0.046(0.090)	0.017(0.086)
		100	Lasso-Cox1	0.007(0.023)	0.001(0.020)
			Lasso-Cox2	0.042(0.063)	0.013(0.057)
0.8	0.3	50	Lasso-Cox1	0.009(0.049)	0.008(0.052)	0.915
			Lasso-Cox2	0.018(0.054)	0.006(0.050)
			Elastic-Net-Cox1	0.006(0.050)	0.003(0.053)
			Elastic-Net-Cox2	0.018(0.054)	0.007(0.047)
		100	Lasso-Cox1	0.012(0.033)	0.006(0.035)
			Lasso-Cox2	0.016(0.034)	0.012(0.028)
			Elastic-Net-Cox1	0.009(0.035)	0.002(0.037)
			Elastic-Net-Cox2	0.016(0.034)	0.011(0.028)
	0.8	50	Lasso-Cox1	0.004(0.027)	0.012(0.027)	0.903
			Lasso-Cox2	0.010(0.033)	0.002(0.059)
			Elastic-Net-Cox1	0.004(0.026)	0.002(0.030)
			Elastic-Net-Cox2	0.010(0.028)	0.002(0.050)
		100	Lasso-Cox1	0.003(0.018)	0.009(0.019)
			Lasso-Cox2	0.009(0.019)	0.004(0.020)
			Elastic-Net-Cox1	0.003(0.018)	0.003(0.019)
			Elastic-Net-Cox2	0.009(0.019)	0.004(0.021)