Faculty, Staff and Student Publications

Language

English

Publication Date

1-1-2026

Journal

Statistics and Its Interface

DOI

10.4310/sii.260602011558

PMID

42232732

PMCID

PMC13225772

PubMedCentral® Posted Date

6-2-2026

PubMedCentral® Full Text Version

Author MSS

Abstract

Effective risk stratification is essential for providing tailored therapies, improving patient outcomes, and optimizing healthcare resources by identifying sub-populations with similar health risks. However, accurate risk ranking is challenging in the presence of heterogeneous subgroups. Under these instances, subgroup-level information can be leveraged to refine the overall risk ranking. We propose a novel approach that integrates within-subgroup risk ranking percentiles to enhance the overall cohort risk stratification. This method uses both a global model and subgroup-specific models along with optimized weights to improve discriminatory performance across the entire cohort. The proposed method is validated through extensive simulations and applied to a study of end-stage renal disease patients awaiting kidney transplantation.

Keywords

Risk stratification, Subgroup heterogeneity, Information borrowing, Subgroup weighting

Published Open-Access

yes

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