j Birthweight (as a continuous variable) model adjusted for age same variables as LBW model.
Optimized cutoffs were defined using logistic regression models, where diagnosis was the response variable and a binary classifier (biomarker < cutoff) was the predicting variable (models adjusted for age and sex).
In a secondary analysis, we also ran CCI as a continuous variable in a model adjusting for BMI as a continuous variable.
To determine factors affecting mortality, two Cox Regression models were performed for each variable: Model 1 adjusted for age, gender and CCI; Model 2 adjusted for age, gender, CCI and frailty status.
All variables were entered into the model adjusting one another.
To assess the predictors for change in the MADRS score (continuous variable) and for remission and response (both dichotomous variables), regression models adjusting for cluster effect caused by sampling from several centers were estimated.
To assess the predictors for change in the MADRS score (continuous variable) and for remission and response (both dichotomous variables), regression models adjusting for cluster effects within center were estimated.
*Time of measurement (week 12, 25 or 33) was included as a variable in the model #Adjusted for the following maternal characteristics: smoking, education, parity, age and BMI before pregnancy and time of measurement (gestational week 12, 25 or 33).
Analysis of multicollinearity This initial step used correlation analysis to identify and address multicollinearity between variables, with the model adjusted to address it.5.5
Therefore, a two-variable logistic regression model adjusted for time at risk was used to specify the individual risk associated with levofloxacin, ciprofloxacin, and each of the other candidate risk factors.
