Logistic push: a regression framework for partial AUC optimization

TL;DR

This paper introduces logistic push, a simple and efficient regression framework for optimizing partial AUC in clinical prediction models, especially useful in high-dimensional settings.

Contribution

The paper proposes a novel logistic regression-based method called logistic push for partial AUC optimization, simplifying marker selection and model fitting.

Findings

Effective in high-dimensional data

Reduces computational complexity

Enhances clinical utility of prediction models

Abstract

The area under the receiver operating characteristic curve (AUC) is often used to evaluate the performance of clinical prediction models. Recently, a more refined strategy has been proposed to examine a partial area under the curve (pAUC), which can account for differing costs associated with false negative versus false positive results. Such consideration can substantially increase the clinical utility of prediction models depending on the clinical question. Properties of the pAUC estimator create significant challenges for pAUC-optimal marker selection and model building. As such, current approaches towards these aims can be complex and computationally intensive. We present a simpler method based on weighted logistic regressions. We refer to our strategy as logistic push, due to shared heuristics with the ranking algorithm P-norm push. Logistic push is particularly useful in the high-dimensional setting, where fast and broadly available algorithms for fitting penalized regressions can be used for both marker selection and model fitting.