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The predictive performance of the final Cox regression model was assessed by internal validation using bootstrap resampling.
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To ensure that the calculated model of the recovering mice vs. sedentary mice is reliable and the observed clustering has not been obtained by chance, we performed an internal validation using 7-fold cross-validation [33].
We performed internal validation using tenfold cross-validation to investigate model overfitting and correct for it.
The model was retested for internal validation using bootstrap, with 1000 bootstrap replicates.
Validation consisted of calibration, internal validation using Bootstrapping technique (Bradley and Tibshirani, 1993) and application of the reduced nomogram model to the external validation cohort.
Internal validation using bootstrapping showed a minimal decrease in the C statistic from 0.836 to 0.835.
Internal validation using bootstrapping confirmed the results only for the pathological nodal status.
Internal validation using bootstrap methods showed similar results (data not shown).
We will perform internal validation using bootstrapping and apply shrinkage to correct for over fit.
Our analysis demonstrates the importance of full internal validation using an independent data set.
The internal validation using 210 spectra resulted in 99.5% correct classifications (Table 3).
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