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

Ryan E Miller1, Patrick Breheny1

  • 1Department of Biostatistics, University of Iowa, Iowa City, IA, USA.

Biometrical Journal. Biometrische Zeitschrift
|February 12, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible method to control false discovery rates in penalized regression models, enhancing feature selection for high-dimensional data. The approach is effective across various models and penalty types, offering improved power for identifying important variables.

Keywords:
Cox regressionfalse discovery ratesgeneralized linear modelshigh-dimensional data analysislassopenalized regression

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Area of Science:

  • Statistics
  • Computational Biology
  • Bioinformatics

Background:

  • High-dimensional data analysis necessitates advanced inferential tools for penalized regression models.
  • Existing methods for false discovery rate control in penalized regression are limited, primarily focusing on lasso-penalized linear models.
  • There is a growing demand for versatile methods applicable to diverse penalized regression techniques and outcomes.

Purpose of the Study:

  • To develop a general method for controlling the marginal false discovery rate in penalized likelihood-based models.
  • To extend false discovery rate control beyond lasso-penalized linear regression to models like logistic and Cox regression.
  • To provide a fast, flexible, and powerful tool for feature selection in high-dimensional statistical analysis.

Main Methods:

  • Derivation of a novel theoretical framework for marginal false discovery rate control.
  • Application of the method to various penalized likelihood models, including logistic and Cox regression.
  • Compatibility with multiple penalty functions such as lasso, elastic net, MCP, and MNet.

Main Results:

  • The proposed method is theoretically valid under specified assumptions and demonstrated to be robust, though slightly conservative, when assumptions are violated.
  • Simulation studies confirm the method's performance and highlight its potential for increased power in selecting causally important features compared to existing approaches.
  • Practical utility is validated through application to gene expression datasets with both binary and time-to-event outcomes.

Conclusions:

  • The developed method offers a significant advancement in controlling false discovery rates for a broad range of penalized regression models.
  • It provides a powerful and flexible alternative for feature selection in high-dimensional data, particularly in bioinformatics and statistical genetics.
  • The approach enhances the reliability and interpretability of results from complex statistical models used in biological research.