Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker.

ABSTRACT:

Background

Risk prediction models for time-to-event outcomes play a vital role in personalized decision-making. A patient's biomarker values, such as medical lab results, are often measured over time but traditional prediction models ignore their longitudinal nature, using only baseline information. Dynamic prediction incorporates longitudinal information to produce updated survival predictions during follow-up. Existing methods for dynamic prediction include joint modeling, which often suffers from computational complexity and poor performance under misspecification, and landmarking, which has a straightforward implementation but typically relies on a proportional hazards model. Random survival forests (RSF), a machine learning algorithm for time-to-event outcomes, can capture com

SUBMITTER: Pickett KL 

PROVIDER: S-EPMC8520610 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature