Table 2

Linear regression with Y ~ N ((X-2)2, ϕ)

R2 = 0.1

R2 = 0.5

R2 = 0.8

bias

cover

r.prec.

bias

cover

r.prec.

bias

cover

r.prec.


MCAR, X ~ normal

CData

-1

95

100

0

95

100

0

95

100

CCase

0

95

64

0

95

64

0

95

64

Passive

-31

86

110

-31

48

55

-30

32

21

PMM

-2

93

62

1

93

61

2

90

47

JAV

-1

94

64

0

93

63

0

92

52

MAR, X ~ normal

CData

0

94

100

0

95

100

0

94

100

CCase

-14

92

54

-9

88

37

-4

91

30

Passive

-41

80

108

-38

45

52

-32

48

18

PMM

-10

88

42

4

88

26

16

51

10

JAV

0

93

41

18

68

21

22

19

10

MAR, X ~ log normal

CData

2

94

100

0

95

100

0

95

100

CCase

-12

94

44

-8

94

27

-4

94

20

Passive

-41

96

81

-25

87

18

-9

90

4

PMM

-10

88

29

8

91

12

35

70

3

JAV

7

92

27

41

70

6

71

20

2


Table 2 Percentage bias, coverage and relative precision for quadratic term in linear regression when Y ~ N ((X-2)2, ϕ). For MCAR, X ~ normal, the maximum MCSEs among the five methods are 1, 0 and 0% for R2 = 0.1, 0.5 and 0.8, respectively. For MAR, X ~ normal, they are 1, 1 and 1%. For MAR, X ~ log normal, they are 2, 2 and 2%

Seaman et al. BMC Medical Research Methodology 2012 12:46   doi:10.1186/1471-2288-12-46

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