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Table 4 Multiple Logistic Regression Models for cancer vs no cancer (no nodules, solid nodules, and ground glass opacities groups) based on log transformed biomarkers on 149 subjects with complete data.

From: Identification of an autoantibody panel to separate lung cancer from smokers and nonsmokers

A: All Biomarkers.

     
 

Estimate

Std. Error

z value

Pr(>|z|)

 

(Intercept)

6.39

2.52

2.54

0.01

 

P53

-0.11

0.48

-0.22

0.82

 

C-myc

0.93

0.56

1.66

0.10

 

IMP1/Koc

-0.21

0.56

-0.37

0.71

 

P62/IMP2

-0.30

0.49

-0.60

0.55

 

IMP3

0.14

0.67

0.21

0.84

 

Cyclin A

2.69

0.79

3.41

<0.01

 

Cyclin B1

-0.84

0.69

-1.22

0.22

 

Cyclin D1

-2.70

0.83

-3.27

<0.01

 

CDK2

1.32

0.67

1.95

0.05

 

Survivin

2.39

0.91

2.62

0.01

 

AIC

86.60

    

10 fold Cross Validation

91%

    

B: Stepwise Multiple Logistic Model.

     
 

Estimate

Odds Ratio

Std. Error

z value

Pr(>|z|)

(Intercept)

6.95

1043.15

2.25

3.09

<0.01

C-myc

0.80

2.22

0.53

1.53

0.13

Cyclin A

2.59

13.32

0.71

3.64

<0.01

Cyclin B1

-0.87

0.41

0.63

-1.38

0.17

Cyclin D1

-2.73

0.06

0.69

-3.94

<0.01

CDK2

1.27

3.56

0.60

2.11

0.04

Survivin

2.44

11.47

0.89

2.75

0.01

AIC:

79.40

    

10 fold Cross Validation

91%

   Â