Statistical analysis
confusion
REGRESSION, CPH
Statistical analyses were conducted in SAS software (Cary, NC, U.S.) version 9.4 and using R version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria). Multivariable analyses using LR and CPH with stepwise selection of variables were performed in SAS software. Odds ratio (OR) and hazard ratio (HR) supplemented with a 95% confidence interval (95% CI) were derived from multivariable regression. For all statistical analyses, we considered a two-sided p value < 0.05 as statistically significant.Artificial neural networks were developed using R language and ‘neuralnet’ package. A resilient backpropagation algorithm (‘Rprop + ’) was utilized with one layer and four hidden neurons (vertices) to predict 5-year survival outcomes. Only factors that were significantly associated with mortality in multivariable analyses with LR were used as input variables to build a neural network. Cross-entropy was used as a differentiable function for the calculation of the error. The threshold specifying the partial derivatives of the error function as stopping criteria in neural network creation was set as 0.1. Each learning process for ANN included two repetitions.Separate predictive models for 5-year OS and 5-year CSS were developed using both LR and ANN in the training cohort. Then, all models were tested in the validation cohort. The area under the curve (AUC) for receiver operating characteristic (ROC) and model accuracy derived from the confusion matrix were calculated. Accuracy was defined as the proportion of correct predictions to all predictions.