Although the results of the above study are still preliminary, if confirmed, they suggest significant pathogenic capability of binary toxin in combination with toxins A and B in the hamster model as also shown by Kuehne et al., which could account for the increased mortality and disease severity associated with binary toxin-containing C. difficile strains infecting humans.
For IHM, logistic regression analyses were performed with mortality as a binary outcome using the same independent variables for those with and without diabetes and for the entire population to assess the influence of diabetes on IHM.
In the first experiment where embryos were raised singly, we analysed embryo mortality as binary response variable with logistic mixed-effect models (every embryo as one independent data point; dead before hatching or hatched).
Glucose complexity, but not SAPS II score, was significantly associated with mortality in a binary logistic regression analysis.
In order to test the time trend for IHM, logistic regression analyses were performed with mortality as a binary outcome, using year of discharge, sex, age, and CCI as independent variables.
This is often the case with binary data AEs such as surgical site infections and surgical mortality.
First, we consider a simple probit model to measure the impact of invasive treatment on in-patient mortality, with in-patient mortality as a dependent binary variable (0/1), controlling for the patient's age and comorbidities.
Multivariable logistic regression model with hospital mortality as the binary outcome adjusted for the variables listed, age, Hunt Hess, and DCI.
Finally, in order to identify independent associations between SD and outcome we fitted a multivariable logistic regression model with hospital mortality as the binary outcome.
Multiclassification with Binary Comparison (BC).
