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Table 4 Binary logistic regression models and their evaluations

From: A new postural stability-indicator to predict the level of fear of falling in Parkinson’s disease patients

(a) Binary logistic regression outcomes based on FTR1,FTR2, and FTR1/2 parameters

Models

Odds ratio

p-value

95% CI for odds ratio

Lower

Upper

 FTR1

1.54

0.005*

1.14

2.07

 FTR2

1.65

0.01*

1.13

2.42

 FTR1/2-ground

33.55

0.005*

2.91

386.72

 FTR1/2-40 cm

30.55

0.005*

2.78

335.73

(b) Receiver operating characteristic (ROC) curve analysis and corrected Akaike’s Information Criterion (CAIC) of FTR1, FTR2, and FTR1/2

 

FTR1

FTR2

FTR1/2-ground

FTR1/2-40 cm

 AUC

0.921

0.932

0.939

0.921

 95% CI for AUC

0.830 to 1

0.845 to 1

0.860 to 1

0.835 to 1

 p-value

< 0.0001**

< 0.0001**

< 0.0001**

< 0.0001**

 Overall accuracy

86.84

89.47

92.1

89.5

 Sensitivity

89.29

92.9

96.43

96.43

 Specificity

90

80

80

70

 CAIC

24.1

22.58

21.56

22.9

Optimal cutoff value

≤ 59.43

> 21.36

≤ 2.83

≤ 2.71

(c) Cross-validation results

 Error

15.79

21.05

10.53

13.16

 Sensitivity

82.14

71.43

92.86

92.86

 Specificity

90

88.57

80

70

  1. AUC Area Under the Curve, CI Confidence Interval, FTR Functional Time Ratio
  2. **p-value ≤ .0005; *p-value < .05