Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach.

ABSTRACT:

Background

The individual data collected throughout patient follow-up constitute crucial information for assessing the risk of a clinical event, and eventually for adapting a therapeutic strategy. Joint models and landmark models have been proposed to compute individual dynamic predictions from repeated measures to one or two markers. However, they hardly extend to the case where the patient history includes much more repeated markers. Our objective was thus to propose a solution for the dynamic prediction of a health event that may exploit repeated measures of a possibly large number of markers.

Methods

We combined a landmark approach extended to endogenous markers history with machine learning methods adapted to survival data. Each marker trajectory is modeled using the in

SUBMITTER: Devaux A 

PROVIDER: S-EPMC9275051 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature