Author Identifier (ORCID)
Wai H. Lim: https://orcid.org/0000-0002-3410-3572
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.
Keywords
education, kidney transplantation, machine learning, prediction modeling, survival analysis, transplantation
Document Type
Journal Article
Date of Publication
1-1-2026
PubMed ID
42061560
Publication Title
Kidney International
Publisher
Elsevier
School
School of Medical and Health Sciences
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Gately, R., Sabanayagam, D., Lim, W. H., Zhu, L., Boroumand, F., Bakar, S., Teixeira-Pinto, A., & Wong, G. (2026). Advances in survival analyses: machine learning methods and model comparison. Kidney International. Advance online publication. https://doi.org/10.1016/j.kint.2026.02.041