Logo image
Back
Marginal false discovery rate control for likelihood-based penalized regression models
Journal article   Peer reviewed

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

Ryan E Miller and Patrick Breheny
Biometrical journal, Vol.61(4), pp.889-901
07/2019
DOI: 10.1002/bimj.201800138
PMID: 30742712
url
https://arxiv.org/pdf/1710.11459View
Open Access

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 datasets with binary and time-to-event outcomes.
 Biometry - methods False Positive Reactions Gene Expression Profiling Humans Likelihood Functions Lung Neoplasms - epidemiology Lung Neoplasms - genetics Regression Analysis Smoking Survival Analysis

Details

Metrics

18 Record Views
9 Times Cited - Web of Science
Logo image