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We carried out a logistic regression analyses excluding those cases which were HIV-positive.
A logistic regression analyses were used to identify factors associated with ABC consumers.
A logistic regression analyses was performed with the significant variables from Table 3 as covariates.
In order to analyse which of the variables present in the index episode were able to predict long-term outcome, a logistic regression analyses was performed.
In a logistic regression analyses adjusted for age, sex, and BMI, prediabetic subjects, and even more T2DM subjects by OGTT, had greater chance to have IR and impaired insulin secretion.
To account for potential confounders of these results, we conducted a logistic regression analyses to adjust for baseline characteristics (age, gender, smoking, alcohol use, clinical T and N stage).
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The age of the subject at inclusion was considered to be a confounder, and all logistic regression analyses were performed while adjusting for age.
Using a model where the disease risk should equal to the genetic factor, each marker has been tested (corresponding to the status) with a logistic-regression analyses.
A binary logistic regression analyses were conducted to test the association between all four ratings in GAD-7 (anxiety) and PHQ-9 (depression) scales subitems as covariates, and migraine vs no headache as the outcome.
In addition, a multivariate logistic regression analyses between the independent and dependant variables, was used to identify variables associated with adherence to ACT.
Using scanner type (Achieva or Ingenia) as a covariate, logistic regression analyses were performed between disease status and individual FA and metabolite values.
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