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Multivariable analyses for response and PFS utilized a multiple logistic regression and a Cox proportional hazards model, respectively.
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In order to predict the likelihood of utilization of PMTCT/HIV-test in both study subjects (not utilized and utilized), multiple logistic regression was used by controlling the effect of confounding variables if any exist and enter method was used.
To control for possible confounding factors, a multiple logistic regression will be utilized with burnout as the dependent variable.
To evaluate a significant association between maternal treatment choice and the baseline variables, a multiple logistic regression analysis model was utilized.
A multiple logistic model was applied with two causes of death, cardiovascular disease and diabetes mellitus, as outcome variables.
A multiple logistic regression model was developed in group A for DIC diagnosis.
A multiple logistic regression analysis was used to determine whether an independent variable was statistically significant.
A multiple logistic regression analysis was done using more stringent criteria (FDR p ≤ 0.00001 and ≥2-fold change), to select candidate genes for high myopia biomarkers.
Potential confounders (age and sex) were included in a multiple logistic regression analysis.
A multiple logistic regression analysis revealed 8 variables that were significant (P <.05) in predicting breast feeding cessation.
The inflation probabilities can be modelled using a multiple logistic.
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