Table 2.

Simulation results based on 1000 Monte Carlo data sets when the true model is the AFT model with baseline density f₀(t). For right-censored data, SNP, BJ, Gehan, and LR indicate fitting using the SNP approach with K and the base density chosen via HQ, the Buckley-James method, and the rank-based method of Jin et al. (2003) using Gehan-type and log-rank weight functions, respectively. All other entries are as in Table 1. For interval-censored data, only SNP was used. True value of β = 2.0 in all cases.

f0(t)	n	Cens. rate	Method	Mean	SD	SE	CP	AFT	PH	PO	Correct
Right-censored data
lognormal	200	25%	SNP	2.02	0.27	0.26	94.4	80.2	8.0	12.3	80.2
			BJ	2.02	0.27	0.26	94.2
			Gehan	2.02	0.27	0.28	95.3
			logrank	2.01	0.29	0.29	94.7
	200	50%	SNP	2.02	0.30	0.30	93.3
Weibull	200	25%	SNP	2.00	0.14	0.15	95.9	71.0	88.7	1.1	98.9
			BJ	2.00	0.18	0.19	96.3
			Gehan	2.00	0.17	0.17	95.7
			logrank	2.00	0.14	0.15	96.1
	200	50%	SNP	2.01	0.20	0.20	94.8
gamma	200	25%	SNP	2.00	0.20	0.19	94.0	65.0	66.4	11.4	65.0
			BJ	2.00	0.22	0.23	96.0
			Gehan	2.00	0.21	0.22	95.4
			logrank	2.00	0.20	0.21	95.4
	200	50%	SNP	2.00	0.26	0.24	92.9
	500	25%	SNP	2.00	0.06	0.06	94.0	98.8	1.2	0.0	98.8
log-mixture	200	25%	SNP	1.99	0.19	0.18	91.9	100.0	0.0	0.0	100.0
			BJ	1.99	0.42	0.28	80.5
			Gehan	1.99	0.29	0.29	95.6
			logrank	2.00	0.41	0.43	96.5
	200	50%	SNP	1.98	0.23	0.22	91.5
	500	25%	SNP	2.00	0.05	0.05	94.8
	500	50%	SNP	2.00	0.06	0.06	93.5
Interval-censored data
gamma	200	20% right, 80% interval	SNP	2.01	0.22	0.21	92.2
gamma	500	16% right, 84% interval	SNP	2.00	0.06	0.06	94.7
log-mixture	200	17% right, 83% interval	SNP	2.05	0.27	0.23	90.3
log-mixture	500	17% right, 83% interval	SNP	2.00	0.07	0.06	94.0