Robust inference for risk heterogeneity under group imbalance

2026-05-30

AI Summary

Existing methods for detecting risk heterogeneity in population-imbalanced biomedical data are often compromised by misspecification of baseline models and regularization bias, leading to unstable inference. This work proposes a robust semiparametric inference framework grounded in Neyman orthogonality, which mitigates the influence of nuisance parameter estimation errors through orthogonalization, thereby enabling more accurate and stable identification of heterogeneity in finite samples. The proposed estimator enjoys favorable theoretical properties, including local robustness and asymptotic normality. In both simulation studies and real-world analysis of eICU data, the method successfully uncovers ethnicity-specific diagnostic risk disparities that standard likelihood-based approaches fail to detect, demonstrating substantial clinical relevance.

Abstract

Population-level heterogeneity is ubiquitous in biomedical data, where differences across demographic or clinical subgroups can substantially alter risk patterns. For example, in intensive care unit (ICU) studies, the mortality risk associated with specific admission diagnoses can vary across ethnic groups. Existing approaches for detecting risk heterogeneity are often sensitive to baseline model misspecification and regularization bias, both of which commonly arise in practice. In this paper, we propose a robust framework for inferring risk heterogeneity between two populations using Neyman orthogonality, which yields estimators that are locally insensitive to nuisance parameter estimation error. The proposed estimator is consistent and asymptotically normal, and simulation studies demonstrate that in finite samples our method substantially reduces bias and improves inferential stability compared with standard likelihood-based approaches. In an application to the eICU Collaborative Research Database, our method reveals clinically meaningful ethnicity-specific heterogeneity in admission diagnoses for in-hospital mortality that standard likelihood-based methods fail to detect.

Problem

• risk heterogeneity
• group imbalance
• model misspecification
• regularization bias
• population-level heterogeneity

Innovation

• risk heterogeneity
• group imbalance
• Neyman orthogonality
• robust inference
• nuisance parameter