Dynamic survival analysis via a landmarking-gradient boosting approach and its application to kidney transplant data.

ABSTRACT:

Background

In some survival studies, longitudinal biomarkers, along with baseline covariates, play crucial roles in predicting patient survival. Dynamic prediction models that incorporate updated longitudinal marker information offer updated survival predictions for patients. In this study, we employ a combination of the nonparametric gradient boosting machine learning algorithm and the landmark approach, which not only facilitates dynamic prediction but also circumvents the limitations of classical methods.

Methods

We conducted two simulation studies under different scenarios to compare three dynamic prediction models: the joint model, the Cox landmarking model, and the Landmarking Gradient Boosting Model (LGBM). We compared the three dynamic survival prediction methods usi

SUBMITTER: Shabani N 

PROVIDER: S-EPMC12513103 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature