Table 3.

Performance of JEAIC and JEBIC compared with JLIC for scenarios with binary outcomes

Setups	Method	C(ρ)	1	2	3	4	5	6	7	8	9	10	Total
m = 0.1	JLIC	AR1	0	0	0.03	0	0	0.007	0.007	0	0	0.003	0.047
		EXC	0.006	0	0.645	0	0	0.132	0.147	0	0	0.023	0.953
		IND	0	0	0	0	0	0	0	0	0	0	0
		Total	0.006	0	0.675	0	0	0.139	0.154	0	0	0.026	1
	JEAIC	AR1	0	0	0.006	0	0	0.001	0.003	0	0	0.001	0.011
		EXC	0.002	0	0.698	0	0	0.138	0.128	0	0	0.023	0.989
		IND	0	0	0	0	0	0	0	0	0	0	0
		total	0.002	0	0.704	0	0	0.139	0.131	0	0	0.024	1
	JEBIC	AR1	0	0	0.011	0	0	0	0	0	0	0	0.011
		EXC	0.011	0	0.952	0	0	0.017	0.009	0	0	0	0.989
		IND	0	0	0	0	0	0	0	0	0	0	0
		total	0.011	0	0.963	0	0	0.017	0.009	0	0	0	1
m = 0.2	JLIC	AR1	0	0	0.057	0	0	0.011	0.009	0	0	0.001	0.078
		EXC	0.008	0	0.63	0.002	0	0.12	0.137	0	0.025	0	0.922
		IND	0	0	0	0	0	0	0	0	0	0	0
		Total	0.008	0	0.687	0.002	0	0.131	0.146	0	0.025	0.001	1
	JEAIC	AR1	0.001	0	0.016	0	0	0.002	0.004	0	0	0.001	0.024
		EXC	0.001	0	0.687	0.001	0	0.146	0.12	0	0	0.021	0.976
		IND	0	0	0	0	0	0	0	0	0	0	0
		total	0.002	0	0.703	0.001	0	0.148	0.124	0	0	0.022	1
	JEBIC	AR1	0.001	0	0.022	0	0	0	0	0	0	0	0.023
		EXC	0.026	0	0.922	0	0	0.015	0.014	0	0	0	0.977
		IND	0	0	0	0	0	0	0	0	0	0	0
		total	0.027	0	0.944	0	0	0.015	0.014	0	0	0	1

The sample size n = 500, T = 3, ρ = 0.3 across 1,000 Monte Carlo datasets. Ten candidate models are considered: {1} = {x₁}, {2} = {x₃}, {3} = {x₁, x₂}, {4} = {x₁, x₃}, {5} = {x₃, x₄}, {6} = {x₁, x₂, x₄}, {7} = {x₁, x₂, x₃}, {8} = {x₁, x₃, x₄}, {9} = {x₂, x₃, x₄}, {10} = {x₁, x₂, x₃, x₄}. It is noted that Model {3} = {x₁, x₂} with an EXC correlation structure is the true model. The variables x₃ and x₄ are redundant.

Abbreviation: EXC, exchangeable; IND, independence; JEAIC, joint empirical Akaike information criterion; JEBIC, joint empirical Bayesian information criterion; JLIC, joint longitudinal information criterion; MLIC, missing longitudinal information criterion; QICW, weighted quasi-likelihood information criterion.

The bold values denote the true mean and correlation structures.