To assess for an association between in-hospital mortality, hemodialysis requirement, ventilator requirement and inotrope requirement we utilized a logistic regression model that adjusted for gender, age, urgency of procedure and American Society of Anesthesiologists (ASA) score.
Combining these measures, we utilized a logistic regression method (see Material and Methods) trained by carefully selected sets of 213 true positive and 173 negative interactions, allowing us to assess the quality of each interaction by a confidence score between 0 and 1.
We utilized those expert opinions to develop a logistic regression model that predicted the probability of sustained yield management in central New York while exploring the factors that influence sustained yield management.
As hypothesized, a logistic regression model that utilized a panel of biomarkers had substantially superior performance to single biomarkers for differentiating sepsis patients with ARDS from those without ARDS as evaluated by ROC curve analysis.
In the final step, to assess the simultaneous effect of different risk factors or risk indicators on the presence of any DR, a logistic regression model was utilized (Table 3).
For part one of the applied models a logistic regression analysis was chosen to predict whether or not patients would utilize resources related to the respective costs figure.
First we utilize the logistic regression model to distinguish the most important symptoms of influenza infection by fitting a logistic regression model to the binary influenza infection outcome in the sample, using binary indicators of the influenza-like symptoms as predictors.
We applied a logistic regression methodology.
The present study utilizes a binomial logistic regression with a fixed lag function, which is very close to a Poisson regression for these large populations, but with the extension of allowing for linear and nonlinear spline functions.
The approach utilizes a multinominal logistic regression technique and provides information on the predicted proportion of defective designs that may occur as a result of workplace and organizational problems.
