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In the Cox proportional hazards regression, we adjusted the model for confounders that were selected from demographic and clinical characteristics using the backward elimination strategy [ 22].
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*The intervention effect and covariates are tested in 2 different panel logistic regression models; simple not controlling for any covariates, full model controlling for confounders.
Relationship between ARF etiology and hospital mortality was assessed using a multivariable regression model adjusting for confounders.
The multivariate linear regression model controlled for confounders (gender, ASA, body mass index, race, smoking status, anesthesia type and comorbidities) previously found to be associated with the outcome (i.e. prolonged LOS).
Note that this model controlled for confounders, day of week effects, and time trends in a city-specific fashion.
Interactions between birth weight and body silhouettes were also tested in the model adjusted for confounders, but none was statistically significant.
All physical tests analyzed with a general linear model adjusted for confounders showed significant differences between cases and controls (Table 5).
Potential mediators were subsequently evaluated individually in models adjusting for confounders that met inclusion criteria for the model, as well as maternal age.
Because there are many other risk factors for low birth weight, we have adjusted for these using a logistic regression model for these confounders.
This approach will be compared with adjusting the regression model for potential confounders, such as history of cervical/bowel screening, and socioeconomic (SEC) status.
Regression models controlled for confounders and analyzed physician-to-physician variability.
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