Table 4.

Summary statistics for the estimation of Model (B) λ(t | X₁, X₂) = λ₀(t)exp(β₁X₁ + β₂X₂ + β₃X₁X₂) when ρ = 1.5.

Proportion censored			β1			β2			β3			ρ		Λ(0.3)			Λ(0.7)
	n	Coef	Bias	ES	RE	Bias	ES	RE	Bias	ES	RE	Bias	ES	Bias	ES	RE	Bias	ES	RE
0%	100	PL	-1	17	–	2	24	–	-2	24	–	–	–	0	3	–	–	10	–
		DEL	0	5	9.70	-24	16	2.10	6	17	2.04	–	–	4	3	0.88	15	7	2.02
		DELρ	0	6	9.38	2	23	1.02	-2	18	1.73	9	40	0	3	1.01	0	10	1.04
	400	PL	-1	8	–	1	12	–	0	11	–	–	–	0	1	–	0	5	–
		DEL	2	2	10.1	-24	8	2.08	6	8	1.82	–	–	4	1	0.95	15	4	1.90
		DELρ	1	2	9.75	1	12	1.02	-1	9	1.51	2	19	0	1	1.02	0	5	1.03
30%	100	PL	-2	21	–	3	29	–	-2	28	–	–	–	0	3	–	0	12	–
		DEL	0	6	14.1	-27	19	2.20	4	20	2.09	–	–	4	3	0.95	16	9	1.93
		DELρ	-1	6	13.6	2	28	1.04	-2	21	1.84	12	46	0	3	1.03	0	12	1.05
	400	PL	-1	9	–	1	14	–	0	13	–	–	–	0	2	–	0	6	–
		DEL	1	3	14.2	-26	9	2.17	5	10	1.92	–	–	4	2	0.98	15	4	1.94
		DELρ	0	3	13.7	1	13	1.04	-1	10	1.70	3	21	0	1	1.04	0	6	1.05
50%	100	PL	-2	27	–	4	35	–	-2	36	–	–	–	0	3	–	0	14	–
		DEL	0	6	23.5	-30	23	2.36	0	24	2.23	–	–	4	3	1.00	16	10	1.93
		DELρ	0	6	22.6	2	33	1.09	-4	25	2.11	14	53	0	3	1.06	0	14	1.08
	400	PL	-1	12	–	1	16	–	0	16	–	–	–	0		–	0	7
		DEL	2	3	21.3	-29	11	2.19	2	11	2.05	–	–	4	2	0.99	16	5	1.88
		DELρ	1	3	20.6	0	16	1.03	-2	11	1.90	3	23	0	2	1.04	0	6	1.06

NOTE: β₁, β₂, and β₃ are the regression coefficients, where the true parameter values are (−0.5, 1, −0.5). Λ(t) = t² is the baseline cumulative hazard function evaluated at t; PL, the maximum partial likelihood estimator β̂_PL; DEL, the double empirical likelihood estimator β̂; DEL_ρ, the extended double empirical likelihood estimator β̂_ρ that allows for a different baseline hazard function for the aggregate data; Bias and ES, empirical bias (×100) and empirical standard deviation (×100) of 1,000 regression parameter estimates; RE, the empirical variance of the maximum partial likelihood estimator divided by that of the double empirical likelihood estimators.