No prior sample size calculation was conducted due to a lack of evidence in building a prognostic model for stratifying patients. However, the number of events in this study reached 123, with an exceeding ratio of 10 events per variable in multiple modeling, suggesting sufficient power of estimation.18 Variables with missing values were C-reactive protein (CRP), baseline and post-treatment EBV DNA. The pattern of missing values in the dataset and the combinations of missing values were presented in Supplementary Figure S1, available at Continuous data were given as medians with interquartile ranges (IQR) and compared with the Wilcoxon rank-sum test. Categorical data were reported as frequencies with percentages and compared with the chi-square test, continuity corrected chi-square test, or Fisher's exact test, where appropriate. Survival curves were estimated using the Kaplan–Meier method and compared with the log-rank test. The Cox proportional hazards model was conducted to calculate the corresponding hazard ratios (HRs) and 95% confidence interval (CIs). The patients with missing values in the training dataset were excluded in the univariable analyses. If a variable met the predetermined significance threshold (P = 0.1) in univariable analysis, it would enter further multivariable Cox regression analysis. Variables with the missing data were imputed using multivariate imputation by chained equations algorithm before multiple modeling.19 Model selection in multiple modeling was based on the Akaike information criterion (AIC). A prognostic model was developed using independent risk factors identified in the training set and graphically presented as a nomogram. The discrimination, predictive accuracy, calibration, and clinical usefulness of the prognostic model were assessed using the concordance index (C-index), time-dependent area under curve (tAUC) at different time points, calibration plot, and decision curves analysis, respectively. The robustness of the model was confirmed via bootstrapping with 1000 resamples and validated in an independent validation set.

All statistical analyses were conducted using the R package (version 4.0.2). A P value of less than 0.05 was considered statistically significant.

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