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We performed two multivariate analyses using a backward step-down logistic regression model including early clinical and biological variables associated first with mortality at day 28 and second with intubation, with a p value <0.15 using univariate analysis.
A reduced model selection was then performed using a backward step-down selection process as described previously (Harrell et al, 1996).
A reduced model selection was performed using a backward step-down selection process, and Harrell's concordance index (c-index) was used for quantifying the nomogram accuracy.
Using a backward step-down selection process to select the most informative variables (age, T stage, N stage, tumour grade, age and tumour location) and multivariable Cox regression coefficient analysis, we were able to design a predictive nomogram (Harrell et al, 1996).
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A final model selection for nomogram was performed by a backward step-down selection process using a threshold P-value of <0.05.
As described, a backward step-down selection process was used to generate the most informative nomogram model that included age, T stage, N stage, tumour grade, age and location.
The final model was chosen on the basis of variables that had P<0.10 on a backward step-down selection process.
Model selection was performed by using a backward step-wise procedure.
All independent variables selected in step 1 and 2 were entered in the models, using a backward stepwise procedure.
The corrector step uses a backward difference in space and a forward difference in time.
We used a backward stepwise approach.
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