Statistical analysis
NRI
REGRESSIONS
All patients were randomized into two groups with a 7:3 ratio. The training group was employed to construct the nomogram, while the internal and external validation groups were applied to validate it. Both univariate and multivariate Cox regressions were performed to calculate the significant parameters (P < 0.05). A predictive nomogram was then established to calculate the 1-, 3-, and 5-year CSS for postoperative GNEC cases.To assess discriminative capabilities, the consistency index (C-index) and the time-dependent area under the curve (AUC) were computed using bootstrapping. The higher value of the C-index and AUC denoted greater predictive ability. The 1-, 3-, and 5-year calibration plots (1,000 bootstrap resamples) were used to compare the predicted CSS with what was actually observed. The ideal prediction was shown to be the 45-degree line.The net benefit for a set of threshold probabilities was calculated using decision curve analysis (DCA), which allowed researchers to test how well the model would function as a clinical decision-making tool. The optimum risk score cutoff value was utilized to create a novel risk stratification approach that divides patients into low-, middle-, and high-risk groups using the X-tile software. To compare the variations in CSS among patients in various risk stratification groups, Kaplan-Meier (KM) curves and log-rank tests were used. The C-index, Net Reclassification Index (NRI), Integrated Discrimination Improvement (IDI), and DCA were used to evaluate the new model’s enhanced predictive potential and effectiveness.All statistical computations and visualizations were done using R software version 4.1.2 (