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).