A comparative analysis of parametric, semiparametric and machine learning models for survival prediction

Survival analysis is crucial in medical research. This study provides a comprehensive comparison of parametric, semi-parametric, and machine learning models for predicting disease-free survival (DFS) in colorectal cancer.

Using data from 2,088 patients in a Phase III trial (Project Data Sphere), we evaluated parametric models (Lognormal, Generalized Gamma), semi-parametric models (CoxPH and stratified Cox model) and machine learning models (Random Survival Forests, XGBoost). Performance was assessed using Area under the curve (AUC) and Brier score.

After 10-fold cross-validation, the Random Survival Forest (RSF) model demonstrated superior discriminative performance (1-year AUC: 0.713, 5-year AUC: 0.799) and the best overall prediction accuracy (Brier score at 3 years: 0.148). Weighted Brier scores confirmed its robustness across clinical scenarios (equal weights: 0.144; FN weight=3: 0.179; FP weight=2: 0.205). Parametric and semi-parametric models showed stable intermediate performance (CV AUC: 0.68-0.70), while XGBoost exhibited severe overfitting with near-random CV AUC (0.49-0.57).

Ensemble methods, particularly Random Survival Forest, provide superior predictive accuracy for Disease-free survival compared to traditional models. These advanced techniques offer significant potential for improving risk stratification and personalizing cancer care, though their integration requires addressing interpretability challenges.