A Meta-Learner Framework to Estimate Individualized Treatment Effects for Survival Outcomes.

ABSTRACT: One crucial aspect of precision medicine is to allow physicians to recommend the most suitable treatment for their patients. This requires understanding the treatment heterogeneity from a patient-centric view, quantified by estimating the individualized treatment effect (ITE). With a large amount of genetics data and medical factors being collected, a complete picture of individuals' characteristics is forming, which provides more opportunities to accurately estimate ITE. Recent development using machine learning methods within the counterfactual outcome framework shows excellent potential in analyzing such data. In this research, we propose to extend meta-learning approaches to estimate individualized treatment effects with survival outcomes. Two meta-learning algorithms are considered, T

SUBMITTER: Bo N 

PROVIDER: S-EPMC12440118 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature