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Table 2 Summary statistics of simulationsa with missing data patterns (MCAR, MAR, MNAR)

From: How to deal with missing longitudinal data in cost of illness analysis in Alzheimer’s disease—suggestions from the GERAS observational study

Imputation method for missing data pattern

10 % missing

20 % missing

30 % Missing

40 % missing

Mean cost (€)

Bias (%)

SSE

SEE

CP

Mean cost

Bias (%)

SSE

SEE

CP

Mean cost

Bias (%)

SSE

SEE

CP

Mean cost

Bias (%)

SSE

SEE

CP

Complete sample

2102

59

61

59

61

59

61

59

61

MCAR

 Complete cases

2102

−0.7 (−0.03 %)

63

64

1.00

2121

18 (0.9 %)

68

69

1.00

2156

53 (3 %)

75

76

1.00

2181

79 (4 %)

87

87

0.99

MI MCMC

2150

48 (2 %)

68

58

1.00

2218

115 (5 %)

78

57

0.46

2300

198 (9 %)

93

57

0.03

2410

308 (15 %)

118

57

0.00

MAR

 Complete cases

1871

−231 (−11 %)

56

57

0.00

1723

−379 (−18 %)

54

55

0.00

1624

−478 (−23 %)

59

59

0.00

1499

−603 (−29 %)

59

61

0.00

MI MCMC

2158

55 (3 %)

112

57

0.76

2039

−64 (−3 %)

83

48

0.65

2218

116 (5 %)

202

52

0.38

2683

581 (28 %)

417

69

0.09

MNAR

 Complete cases

1544

−558 (−27 %)

27

28

0.00

1291

−812 (−39 %)

22

22

0.00

1096

−1007 (−48 %)

20

19

0.00

929

−1173 (−56 %)

17

16

0.00

MI MCMC

1602

−501 (−24 %)

28

26

0.00

1383

−720 (−34 %)

22

19

0.00

1212

−890 (−42 %)

20

15

0.00

1043

−1059 (−50 %)

19

11

0.00

  1. % bias was calculated as (estimated−actual)/actual cost × 100), where actual cost was the mean cost for the complete sample
  2. Abbreviations: CP coverage probability, MCMC Markov Chain Monte Carlo, MI multiple imputation, SEE standard error estimate, SSE sampling standard error
  3. a1000 simulations and sample size 1497 for different levels of missing data (10–40 %)