BMC Medical Research Methodology

Table 3 Linear regression with interaction

From: Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods

	R ²= 0.1			R ² = 0.5			R ² = 0.8
	bias	cover	r.prec.	bias	cover	r.prec.	bias	cover	r.prec.
	MCAR, X, Z ~ normal
CData	3	93	100	1	93	100	0	93	100
CCase	-3	95	71	-1	95	71	0	95	71
Passive1	-31	97	136	-19	94	116	-18	88	106
Passive2	-11	95	86	-17	94	115	-17	89	103
PMM	-12	96	86	-15	96	106	-13	91	93
JAV	-2	93	66	-1	94	65	0	94	65
	MAR, X, Z ~ normal
CData	-1	94	100	-2	95	100	0	95	100
CCase	-15	96	82	-12	94	69	-5	95	62
Passive1	-36	99	147	-24	94	112	-25	79	110
Passive2	-14	96	75	-26	94	111	-25	82	89
PMM	-19	97	84	-23	94	94	-17	90	85
JAV	-3	94	60	-4	92	54	1	94	53
	MAR, X, Z ~ log normal
CData	-1	96	100	2	95	100	1	96	100
CCase	-17	94	57	-9	95	38	-4	96	30
Passive1	-43	98	129	-20	96	76	-34	68	65
Passive2	-40	96	71	-42	89	58	-45	73	27
PMM	-40	96	79	-38	92	66	-27	85	30
JAV	-3	93	41	8	92	26	14	92	20

Table 3 Percentage bias, coverage and relative precision for interaction term in linear regression. The true value of the interaction term is 1. For MCAR, X, Z ~ normal, the maximum MCSEs are 4, 1 and 1% for R ² = 0.1, 0.5 and 0.8, respectively. For MAR, X, Z ~ normal, they are 4, 2 and 1%. For MAR, X, Z ~ log normal, they are 6, 3 and 1%

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