Marginal false discovery rate control for likelihood-based penalized regression models

TL;DR

This paper introduces a versatile method for controlling the marginal false discovery rate in various penalized likelihood models, enhancing feature selection accuracy in high-dimensional data analysis.

Contribution

It develops a general, fast, and flexible approach applicable to multiple penalized likelihood models, extending false discovery rate control beyond linear regression.

Findings

Method is valid under certain theoretical conditions.

Approach is robust but slightly conservative in practice.

Often more powerful than existing methods for feature selection.

Abstract

The popularity of penalized regression in high-dimensional data analysis has led to a demand for new inferential tools for these models. False discovery rate control is widely used in high-dimensional hypothesis testing, but has only recently been considered in the context of penalized regression. Almost all of this work, however, has focused on lasso-penalized linear regression. In this paper, we derive a general method for controlling the marginal false discovery rate that can be applied to any penalized likelihood-based model, such as logistic regression and Cox regression. Our approach is fast, flexible and can be used with a variety of penalty functions including lasso, elastic net, MCP, and MNet. We derive theoretical results under which the proposed method is valid, and use simulation studies to demonstrate that the approach is reasonably robust, albeit slightly conservative, when these assumptions are violated. Despite being conservative, we show that our method often offers more power to select causally important features than existing approaches. Finally, the practical utility of the method is demonstrated on gene expression data sets with binary and time-to-event outcomes.