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

Table 4 Logistic regression with quadratic term

(*p, β* ₂)	(0.5, 1/12)			(0.5, 1/6)			(0.1, 1/12)
	bias	cover	r.prec.	bias	cover	r.prec.	bias	cover	r.prec.
	MCAR, X ~ normal
CData	1	95	100	-1	95	100	-6	94	100
CCase	1	96	70	-1	95	73	-8	95	67
Passive	-30	97	137	-30	92	136	-34	99	119
PMM	0	94	67	-1	94	70	-10	93	63
JAV	-7	96	76	-23	92	102	27	91	72
	MCAR, X ~ log normal
CData	6	95	100	4	94	100	4	94	100
CCase	7	94	69	4	95	73	4	96	71
Passive	-36	96	222	-45	90	308	-40	93	127
PMM	8	93	67	6	92	68	5	95	68
JAV	-66	71	178	-118	3	398	55	85	56
	MAR, X ~ normal
CData	0	96	100	1	95	100	-8	96	100
CCase	-1	97	67	0	95	63	-28	96	28
Passive	33	97	125	-30	92	115	-71	99	171
PMM	-2	94	65	-1	92	59	-33	85	27
JAV	37	89	59	56	62	79	51	82	26
	MAR, X ~ log normal
CData	5	93	100	5	96	100	5	94	100
CCase	7	93	70	7	95	69	7	95	38
Passive	-8	98	100	2	99	106	-202	16	81
PMM	8	91	67	7	93	64	5	84	34
JAV	22	92	81	-30	80	105	333	25	7

Table 4 Percentage bias, coverage and relative precision for quadratic term in logistic regression. For MCAR, X ~ normal, the maximum MCSEs are 2, 1 and 3% for (p, β ₂)=(0.5,1/12), (0.5, 1/6) and (0.1, 1/12), respectively. For MCAR, X ~ log normal, they are 2, 2 and 2%. For MAR, X ~ normal, they are 2, 1 and 4%. For MAR, X ~ log normal, they are 2, 2 and 6%

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