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Synapse
May 3, 20261 citations

Advances in survival analyses: machine learning methods and model comparison.

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RGRyan GatelyDSDharshana SabanayagamWLWai H Lim

Key Points

  • This article aims to discuss novel approaches in survival analysis using machine learning techniques.
  • Exploration of machine learning methods for predicting survival outcomes.
  • Comparison of traditional and novel survival analysis models.
  • Provision of R code for practical implementation of the discussed methods.
  • Demonstrated improved predictive performance with machine learning methods over traditional approaches.

Abstract

This is the second article in a two-part series on survival analysis. In part 1, we discussed the core concepts and traditional methods used in survival analysis. Part 2 explores the novel approaches to predict survival outcomes and evaluate model performance. To facilitate hands-on learning and practical implementation, the R code used in these analyses is provided in the supplementary materials, along with instructions to help readers apply these methods to their data.

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Cite This Study

Gately et al. (2026) studied this question.

synapsesocial.com/papers/69f6e6648071d4f1bdfc7006https://doi.org/10.1016/j.kint.2026.02.041
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Advancements in Cancer Survival Prediction: A Systematic Review of Classical and Modern Approaches2025
  2. 2A Systematic Review on Machine Learning Techniques for Survival Analysis in Cancer2025
  3. 3Neutral Benchmarking of Survival Models in Health Sciences: Comparative Study of Classical and Machine Learning Techniques2024
  4. 4Survival analysis for lung cancer patients: A comparison of Cox regression and machine learning models2024 · 5 citations
  5. 5Reduction Techniques for Survival Analysis2025