Advances in survival analyses: machine learning methods and model comparison

Ryan Gately; Dharshana Sabanayagam; Wai H Lim; Lin Zhu; Farzaneh Boroumand; Shuvo Bakar; Armando M Teixeira-Pinto; Germaine Wong

doi:10.1016/j.kint.2026.02.041

Advances in survival analyses: machine learning methods and model comparison

Kidney International 2026 Open access

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Authors: Ryan Gately, Dharshana Sabanayagam, Wai H Lim, Lin Zhu, Farzaneh Boroumand, Shuvo Bakar, Armando M Teixeira-Pinto, Germaine Wong

DOI: 10.1016/j.kint.2026.02.041

Cited by: 1

References: 33

Abstract

This is the second article in a 2-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.

Fields of Study & Subjects

Statistical Methods and Inference Financial Distress and Bankruptcy Prediction Sepsis Diagnosis and Treatment

Publication details

Journal / source: Kidney International
Publisher: Elsevier BV
Volume: 110
Issue: 1
Pages: 78–88
Publication date: 29 April 2026
Language: EN

References 33