Shift-adjusted Neyman–Pearson classifiers via single index modeling (SACSIM)
Year: 2026
Type: article
Abstract: Neyman-Pearson (NP) classifiers, which aim to maximize the clinical benefit while adhering to risk constraints, are crucial in many practical fields, including early cancer detection. However, applying these classifiers can be challenging due to discrepancies between the data distributions of the source and target populations. The potential impact can be disproportionately severe for under-represented groups. We propose a semi-parametric model-based approach for adapting NP classifier decision rules to different populations while equitably controlling classification errors specific to clinical applications. Our method involves a shift-adjustment strategy that leverages a small unlabeled sample from the target population, along with minimal auxiliary information and the labeled source data. This approach enhances the applicability of the learned decision rules and ensures they are consistently tailored for the target population. We demonstrate the performance through theoretical studies and simulations and illustrate the approach with an example of a prostate cancer study.
Source: Biometrics
Authors: Jiaming Qiu, Ying-Qi Zhao, Yingye Zheng
Institution: Fred Hutch Cancer Center
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,780
Awards: Statistical Methods for Prospective Evaluation of Biomarkers, The Early Detection Research Network: Data Management and Coordinating Center, Precompetitive Collaboration on Liquid Biopsy for Early Cancer Assessment: Data Management and Coordinating Unit, Developing methods for advancing the early detection of pancreatic ductal adenocarcinoma leveraging electronic medical records data
OpenAlex ID: W7202147291