Improving Survival Models in Health Care by Balancing Imbalanced Cohorts: A Novel Approach.

ABSTRACT:

Purpose

We explore whether survival model performance in underrepresented high- and low-risk subgroups-regions of the prognostic spectrum where clinical decisions are most consequential-can be improved through targeted restructuring of the training data set. Rather than modifying model architecture, we propose a novel risk-stratified sampling method that addresses imbalances in prognostic subgroup density to support more reliable learning in underrepresented tail strata.

Methods

We introduce a novel methodology that partitions patients by baseline prognostic risk and applies matching within each stratum to equalize representation across the risk distribution. We implement this framework on a cohort of 1,799 patients with resected colorectal liver metastases (CRLM), including

SUBMITTER: Ning C 

PROVIDER: S-EPMC12854512 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature