Independent risk factors were analysed using a multivariate logistic regression.
Analyses of independent predictors for survival from univariate analysis were performed using a multivariate logistic regression.
Odds ratio and 95% confidence intervals were calculated, by using a multivariate logistic regression analysis.
The primary analysis was the assessment of the predictive value of proADM for the primary endpoint using a multivariate logistic regression model adjusted for the clinical risk category.
In a second step, segment-specific cross-category effects are estimated for suitably selected categories using a multivariate logistic modeling framework.
The consistency of the abovementioned prescribing patterns was further confirmed using a multivariate logistic regression model, controlling for onset age, age of enrollment, illness duration, gender, total social outcome, and diagnostic subtype.
This difference was independent of sex, EBV status and histological subtype, shown using a multivariate logistic regression model (P = 0.012).
Significant data were studied using a multivariate logistic regression analysis.
The associations were further explored using a multivariate logistic analysis.
Analysis was performed using a multivariate logistic regression model.
The groups were compared using a multivariate logistic regression model.
