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Table 6 Incorporating covariates into the model. Models treating the fitting of counts for tag GCGAAACCCT from Table 2, with the cell lines hypothetically allocated to normal tissue B (libraries 5 and 6) and cancer tissue B (libraries 7 and 8). This division is made to illustrate how the effects of two differences, normal vs cancer and tissue A vs tissue B (β1 and β2 respectively) can be partitioned according to their importance. In Model 2, we have further introduced a continuous covariate (β3) corresponding to the levels of a biomarker to show how that can be figured in as well.

From: Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates

Model 1: Hypothetical Cov. = 1.224e - 03 df = 5
Coefficients Estimate (s.e) t-value p-value
β 0 -4.928 0.291 -16.921 1.318e - 05
β 1 -1.293 0.593 -2.181 0.0810
β 2 -1.956 0.738 -2.650 0.0454
Model 2: Hypothetical Biom. = 1.254e - 03 df = 4
Coefficients Estimate (s.e) t-value p-value
β 0 -4.167 0.608 -6.851 1.012e-03
β 1 -1.423 0.611 -2.328 0.0674
β 2 -2.031 0.752 -2.700 0.0428
β 3 -1.365 1.028 -1.328 0.2417