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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

From: When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts

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