Table 2.

Estimated regression coefficients and standard errors (SE) when no values are missing; when data are missing completely at random; when outcome blood pressure (BP) is missing at random; when covariate (baseline BP) is missing at random; and when outcome BP is missing not at random. When values were missing, multiple imputation as well as the maximum likelihood method were used

Type of missingness	Analysis	Regression coefficients
		Intercept estimate (standard error (SE)) P	Baseline blood pressure estimate (SE) P	Outcome blood pressure estimate (SE) P
No missing values	Complete case analysis	−2.48 (4.69) 0.60	1.013 (0.025) <0.0001	−50.8 (1.48) P < 0.0001
Missing completely at random (MCAR)	Multiple imputation	−6.11 (5.72) P = 0.29	1.037 (0.030) P < 0.0001	−51.5 (1.78) P < 0.0001
	Maximum likelihood	−6.85 (5.17) P = 0.18	1.041 (0.028) P < 0.0001	−51.2 (1.68) P < 0.0001
Missing at random (MAR) (outcome)	Multiple imputation	−2.60 (5.15) P = 0.61	1.014 (0.028) P < 0.0001	−51.0 (1.70) P < 0.0001
	Maximum likelihood	−2.75 (5.08) P = 0.59	1.015 (0.027) P < 0.0001	−51.2 (1.65) P < 0.0001
Missing at random (MAR) (baseline blood pressure)	Multiple imputation	−6.09 (5.37) P = 0.26	1.026 (0.029) P < 0.0001	−51.1 (2.16) P < 0.0001
	Maximum likelihood	−5.49 (5.41) P = 0.31	1.026 (0.032) P < 0.0001	−50.2 (2.18) P < 0.0001
Not missing at random (MNAR) (outcome blood pressure)	Multiple imputation	−8.64 (5.07) P = 0.089	1.026 (0.028) P < 0.0001	−47.5 (1.99) P < 0.0001
	Maximum likelihood	−8.13 (5.61) P = 0.15	1.026 (0.032) P < 0.0001	−47.6 (2.09) P < 0.0001

For comparison the results of an analysis of the data without any values missing is also shown