Development of the Logistic regression model
Statistical analysis of the data was performed using SPSS 21.0 for
Windows (IBM Corp., Armonk, NY, USA).
To determine which variables were significant, univariable logistic
regression analysis was used.
The variables with a p-value <.10 were used in the
multivariable analysis. This was followed by a backward stepwise manual
selection process, progressively excluding the variable with the highest
p-value (1).
As described by Steyerberg et al., the p-value of 0.10 was used to
prevent a potential incorrect exclusion of a predictive factor. This
would be far more detrimental for the test than missing a potential
discriminating factor (33,34).
Interaction terms were used to test possible interaction between the
significant variables in the model. Furthermore, multicollinearity was
tested. Bootstrap resampling was used for internal validation (n=5000).
(34,35) To correct for over-optimism of the model, regression
coefficients were multiplied by the calculated shrinkage factor(1).