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Table 1 VIF and Type-I Error Control in the presence of a confounder

From: Quantitative gene set analysis generalized for repeated measures, confounder adjustment, and continuous covariates

   

VIF Estimate

Type-I Error

Sample Size

Percent Conf.

True VIF

QuSAGE

Q-Gen

Qusage

Q-Gen

5

0

20.024

19.04

18.89

.046

.054

5

.25

20.024

17.43

18.87

.042

.050

5

.5

20.024

17.89

18.89

.041

.055

5

0

6.435

6.20

6.16

.044

.050

5

.25

6.435

6.282

6.2

.040

.054

5

.5

6.435

8.04

6.16

.045

.055

5

0

1

0.99

0.99

.048

.053

5

.25

1

1.72

1.00

.041

.054

5

.5

1

4.16

0.99

.010

.057

15

0

20.024

19.75

19.74

.047

.047

15

.25

20.024

18.72

19.72

.056

.049

15

.5

20.024

18.59

19.71

.0822

.053

15

0

6.435

6.36

6.36

.053

.052

15

.25

6.435

6.43

6.36

.072

.052

15

.5

6.435

8.27

6.34

.101

.051

15

0

1

1.00

1.00

.048

.049

15

.25

1

1.719

1.00

.097

.050

15

.5

1

4.176

1.00

.074

.051

  1. VIF and type-I error estimates under a case-control simulation in the presence of a confounding variable where the percentage of genes that are affected by the confounder are examined for 0, 25, and 50 % respectively. Error rates in bold indicate they are within the margin of error (0.05 ± 0.00427) for the simulation study. Since Q-Gen allows for a linear model that adjusts for covariates, VIF estimation and the controlling of the type-I error is more consistent than the standard two sample t-testing conducted within QuSAGE
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BMC Bioinformatics

ISSN: 1471-2105

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