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Table 1 Estimates of associations between predictors and health outcome at 70% response-rate

From: Preventing bias from selective non-response in population-based survey studies: findings from a Monte Carlo simulation study

bnon

 

10% most extreme values on the health outcome totally missing

Extreme values on the health outcome not totally missing

CC

FIML

MI

CC

FIML

MI

×1,×2,× 3

Y

Pred.

Bpred (SE)

95% coverage

Bpred (SE)

95% coverage

Bpred (SE)

95% coverage

Bpred (SE)

95% coverage

Bpred (SE)

95% coverage

Bpred (SE)

95% coverage

0.3

0.0

×1

.17 (.04)

88

.18 (.04)

92

.17 (.04)

86

.20 (.04)

92

.20 (.04)

92

.20 (.04)

95

  

×2

.25 (.04)

86

.26 (.04)

92

.25 (.04)

76

.29 (.04)

94

.30 (.04)

93

.29 (.04)

95

0.1

0.1

×1

.17 (.04)

88

.17 (.04)

91

.17 (.04)

88

.20 (.04)

94

.20 (.04)

94

.20 (.04)

95

  

×2

.25 (.04)

81

.26 (.04)

91

.25 (.04)

79

.29 (.04)

95

.29 (.04)

93

.29 (.04)

96

0.3

0.1

×1

.16 (.04)

84

.17 (.04)

88

.16 (.04)

83

.18 (.04)

93

.19 (.04)

93

.18 (.04)

93

  

×2

.24 (.04)

80

.25 (.04)

88

.24 (.04)

69

.27 (.04)

93

.28 (.04)

95

.28 (.04)

91

0.0

0.3

× 1

.18 (.04)

88

.18 (.04)

91

.17 (.04)

88

.20 (.04)

95

.20 (.04)

93

.20 (.04)

96

  

×2

.26 (.04)

84

.26 (.04)

93

.26 (.04)

82

.29 (.04)

97

.29 (.04)

94

.29 (.04)

98

0.1

0.3

×1

.17 (.04)

88

.17 (.04)

88

.17 (.04)

88

.18 (.04)

93

.19 (.04)

95

.18 (.04)

94

  

×2

.25 (.04)

77

.25 (.04)

88

.25 (.04)

76

.28 (.04)

93

.28 (.04)

93

.28 (.04)

93

0.3

0.3

×1

.14 (.04)

70

.14 (.04)

72

.13 (.04)

63

.15 (.04)

75

.15 (.04)

78

.14 (.04)

71

  

×2

.22 (.04)

56

.23 (.04)

71

.21 (.04)

48

.23 (.04)

74

.24 (.04)

83

.23 (.04)

59

  1. The true population values are bpred ×1 = 0.20, bpred ×2 = 0.30. Different degrees of dependency between study variables and non-response are modeled.
  2. Y = health outcome. bnon = regression coefficients of normally distributed liability of non-response (L) on predictors (× 1, × 2, ×3) and on health outcome.
  3. bpred = coefficients for the regression of health outcome on × 1 and × 2, when the outcome is treated as a categorical variable, and the probit-link is used.
  4. SE standard error, 95% coverage percentage of the randomly drawn samples providing a 95% confidence interval containing the true population value. FIML full information maximum likelihood, MI multiple imputation (predictive mean matching). N in the original sample before non-response = 1000. × 3 was included as auxiliary variable in FIML and as predictor in MI. 50 data sets were imputed.