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Table 2 Estimates of associations between predictors and health outcome at 50% 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

.18 (.05)

91

.18 (.05)

92

.17 (.05)

88

.20 (.04)

92

.20 (.05)

92

.20 (.05)

93

  

×2

.25 (.05)

91

.26 (.05)

93

.25 (.05)

76

.29 (.04)

94

.30 (.04)

94

.29 (.05)

93

0.1

0.1

×1

.17 (.05)

89

.17 (.05)

91

.17 (.05)

88

.20 (.04)

92

.20 (.04)

92

.19 (.05)

93

  

×2

.25 (.04)

89

.26 (.05)

93

.25 (.05)

82

.29 (.04)

96

.29 (.04)

95

.29 (.05)

95

0.3

0.1

×1

.16 (.05)

88

.17 (.05)

90

.16 (.05)

84

.18 (.05)

92

.18 (.05)

92

.18 (.05)

90

  

×2

.24 (.05)

84

.25 (.05)

89

.23 (.05)

69

.27 (.04)

92

.28 (.05)

95

.27 (.05)

86

0.0

0.3

×1

.18 (.05)

90

.18 (.05)

93

.17 (.05)

91

.20 (.04)

94

.20 (.04)

94

.19 (.05)

95

  

×2

.26 (.04)

90

.26 (.05)

94

.26 (.05)

85

.29 (.04)

95

.29 (.04)

93

.29 (.05)

95

0.1

0.3

×1

.16 (.05)

88

.17 (.05)

91

.16 (.05)

86

.18 (.05)

92

.18 (.05)

93

.18 (.05)

92

  

×2

.24 (.04)

82

.25 (.05)

91

.24 (.05)

78

.27 (.04)

93

.28 (.04)

95

.27 (.05)

92

0.3

0.3

×1

.12 (.05)

68

.12 (.05)

69

.11 (.05)

54

.13 (.05)

70

.13 (.05)

72

.12 (.05)

60

  

×2

.20 (.05)

58

.21 (.05)

68

.19 (.05)

43

.21 (.05)

68

.22 (.05)

76

.20 (.05)

49

  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, x3) 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. X3 was included as auxiliary variable in FIML and as predictor in MI. 50 data sets were imputed