Statistical analyses
tumor
TUMOR
The sample size was calculated to have a 90% power to detect a difference in treatment effect between biomarker positive versus biomarker negative patients. The treatment effect was defined as the difference in the tumor size evolution between the two arms. Since the observed mean tumor reduction under afatinib was less than the anticipated threshold of 20%All statistical analyses were performed using R software. Comparisons between groups were assessed using the Wilcoxon rank-sum test for quantitative variables, and the Fisher test for qualitative variables. For post-hoc analyses, univariate and multivariate Cox proportional hazard models were performed to identify prognostic biomarkers. The results are presented as hazard ratios (HR) and 95% confidence intervals (CIs). Survival curves were obtained with Kaplan–Meier estimates and compared using the log-rank test. The level of significance was set at 