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Background <p>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.</p> Methods <p>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.</p> Results <p>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).</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月25日

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  • Salomi Arul.,
  • Sujatha V.

摘要

Background

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.

Methods

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.

Results

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).

Conclusions

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.