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We used multivariable logistic regressing models to further adjust for age, income, gender, hypercholesterolemia, body mass index, diabetes, smoking intensity, and time period of smoking cessation.
In order to further adjust for residual confounding due to measured differences between appropriate and inappropriate empiric therapy, a propensity score for the probability of receipt of appropriate empiric therapy was created and added to the final Cox proportional hazard model.
We used unconditional logistic regression analysis to further adjust for maternal age and parity.
We further adjust for age, sex and Body-Mass Index (BMI) at baseline.
We applied the following three analytic strategies to further adjust for confounding by indication.
Models using BMI or WC as an independent variable did not further adjust for height or VAT due to collinearity.
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The results remained unchanged when the model was further adjusted for ethnicity.
P-values were not further adjusted for multiple testing.
For insulin sensitivity and lipid measures the associations were further adjusted for BMI.
NCCM score analysis was further adjusted for conventional cardio-metabolic risk factors.
Analyses were further adjusted for other demographic, health behaviours and health related characteristics.
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