Table 2.

Treatment effect (log odds ratio) estimates from a simulation study of multiple imputation of binary data using multiple models. One hundred models, 2 imputations within each model. Each row represents a different assumption regarding the control group missing data mechanism. Treatment group missing data are assumed to be MAR with no uncertainty throughout.

Ignore Assump.	Uncertainty	Mult.* Mean	Mult.* Ratio**	Bias	RMSE	Cvg.	Width of CI	γ̂	γ̂w	γ̂b	$\frac{{\widehat{\gamma }}^b}{\widehat{\gamma }}$
MAR	None	log(1)	log(1)	0.66	0.75	59.1	1.47	0.60	0.60	0.00	0.00
	Mild	log(1)	log(2)	0.66	0.75	62.8	1.55	0.64	0.60	0.03	0.06
	Moderate	log(1)	log(3)	0.66	0.75	67.9	1.65	0.68	0.60	0.08	0.12
	Ample	log(1)	log(4)	0.66	0.75	72.6	1.74	0.71	0.60	0.11	0.16
Weak NMAR	None	log(2)	log(1)	0.22	0.42	90.6	1.47	0.58	0.59	0.00	0.00
	Mild	log(2)	log(2)	0.22	0.42	91.9	1.53	0.62	0.59	0.03	0.05
	Moderate	log(2)	log(3)	0.23	0.43	93.2	1.62	0.66	0.59	0.07	0.11
	Ample	log(2)	log(4)	0.23	0.43	94.9	1.70	0.69	0.59	0.10	0.15
Strong NMAR	None	log(3)	log(1)	−0.01	0.36	94.9	1.46	0.56	0.57	0.00	0.00
	Mild	log(3)	log(2)	−0.01	0.36	95.5	1.51	0.59	0.57	0.03	0.05
	Moderate	log(3)	log(3)	0.00	0.36	97.0	1.58	0.63	0.56	0.06	0.10
	Ample	log(3)	log(4)	0.00	0.36	97.7	1.65	0.65	0.56	0.09	0.14
Misspec. NMAR	None	log(.5)	log(1)	1.10	1.16	14.0	1.48	0.60	0.60	0.00	0.00
	Mild	log(.5)	log(2)	1.10	1.16	16.2	1.55	0.63	0.60	0.03	0.05
	Moderate	log(.5)	log(3)	1.10	1.16	20.7	1.64	0.67	0.60	0.07	0.11
	Ample	log(.5)	log(4)	1.10	1.16	24.2	1.72	0.70	0.60	0.10	0.15

Mult: multiplier; RMSE: root mean squared error; Cvg: coverage

^*Multipliers drawn from a Normal distribution;

^**Multiplier SD= mult. ratio/3.92