b + - + value for the multivariate analysis calculated with binary logistic regression, including the variables age, gender, risk factors, secondary bacterial pneumonia, and bacteremia.
b P value for the multivariate analysis calculated with binary logistic regression, including the variables age, gender, risk factors, secondary bacterial pneumonia, and bacteremia.
Relationship between a discordant response to items 2 and 12 and clinical variables were analyzed using binary logistic regression, including sex, age, number of previous voluntary or compulsory admission, length of disease, diagnosis, BPRS scores, and C-AES subscales.
On multivariate analysis using binary logistic regression, including grade, size, lymph node, HER2 and ER status, only negative ER significantly correlated with HIF-1 α expression (P=0.039, Hazard ratio=0.289, 95% CI for hazard ratio 0.089 0.941).
Multiple binary logistic regression including risk factors with p < 0.40 in a bivariable model, forward selection, and backward elimination methods (inclusion criteria, p < 0.20) were used to investigate the potential confounding effect of other risk variables.
d P value for the multivariate analysis calculated with binary logistic regression, including the variables age, gender, risk factors, and secondary bacterial pneumonia (bacteremia variable was excluded because it shows a co-lineal relation with the variable mechanical ventilation).
In addition to the binary logistic regression analysis including CHLm (all participants), logistic regressions were performed with CHL, only including participants having a valid CHL from the beginning.
To investigate whether MDI scores or WHOQoL subscales were predictive of sexual distress, binary logistic regression analyses including age and depression score according to MDI and the five WHOQoL subscales (general health, physical health, psychological health, social relationships and environment), were conducted, respectively.
Results for binary logistic regression analysis including all variables together.
Considering the combination of MTH and PCI, we performed binary logistic regression analysis including all patients (n = 584).
A multivariate analysis was performed by using the binary logistic regression model, including all variables that were associated with the outcome (P < 0.05) in univariate analysis.
