Significance was set at P < 0.10, which was used as the criterion for determining variable entry and removal from the multivariate analysis.
Univariate logistic regression analysis was initially conducted, followed by multivariate analysis with 'forced entry' of all variables examined in the univariate analyses into the multivariate regression model.
The multiple correspondence analysis performs a multivariate analysis, with categorical and quantitative variables.
Backward stepwise regression was performed with variable entry when probability was less than 0.05 and removal when probability exceeded 0.10.
In multivariate analysis with forward inclusion of variables with P < 0.10 as found in univariate analysis and F for entry 0.05, smoking, male gender, glucose level, vWF and SAE proved to be significant factors contributing to the formation of AGEs.
A backward "conditional" regression model was used in the multivariate analysis with removal set at a 0.1 level of significance.
These variables were included in multivariate analysis with time-to-graft-failure as the dependant variable.
All significant variables from the univariate analyses were entered into the multivariate analyses in a backward stepwise Cox regression analysis with a probability for stepwise entry and removal at 0.05 and 0.10, respectively.
For stepwise multivariate analyses, the probabilities for entry and removal were 0.05 and 0.10, respectively.
In the multivariate analysis, the Wald forward logistic regression model, with entry and removal criteria of 0.01 and 0.05, or 0.05 and 0.10, respectively, was used to identify independent predictors of acute appendicitis.
Only variables with significant P-values from the univariate analyses were entered into the multivariate analysis, using the Cox proportional hazards model (backward stepwise, probability for stepwise entry, and removal set at 0.05 and 0.10).
