Table 2.

The partial log-likelihood and C-index values over 50 simulated replicates under different simulated scenarios

Simulated scenarios	Methods	Partial log-likelihood	C-index
Scenario 1	lasso: sλ = 0.045a	−1200.49(62.769)	0.680(0.022)
	sslasso: s0 = sλ	−1201.039(63.520)	0.679(0.022)
Scenario 2	lasso: sλ = 0.052a	−1204.975(49.157)	0.735(0.019)
	sslasso: s0 = sλ − 0.02	−1198.005(49.900)	0.740(0.020)
Scenario 3	lasso: sλ = 0.057a	−1150.189(60.427)	0.795(0.016)
	sslasso: s0 = sλ − 0.03	−1141.872(61.211)	0.800(0.016)
Scenario 4	lasso: sλ = 0.029a	−1195.046(52.336)	0.624(0.025)
	sslasso: s0 = sλ+0.01	−1192.936(51.836)	0.625(0.028)
Scenario 5	lasso: sλ = 0.034a	−1153.503(53.632)	0.785(0.015)
	sslasso: s0 = sλ − 0.02	−1140.097(54.464)	0.790(0.015)
Scenario 6	lasso: sλ = 0.042a	−1159.421(55.534)	0.792(0.018)
	sslasso: s0 = sλ − 0.02	−1143.262(57.398)	0.801(0.018)

^*Note: Scenario 1, 2 and 3: n = 500, m = 200; Scenario 4, 5 and 6: n = 500, m = 1000. The slab scales, s₁, are 0.5 in all scenarios. The optimal models for different scenarios are summarized here. More results can be found in Supplementary Table S1

^as_λ denotes the average value of penalty parameters s_λ (s_λ = 1/λ) of 50 repeated samples, where λ is the optimal value of the penalty parameter for the lasso. ‘sslasso’ represents the spike-and-slab lasso Cox model