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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Variable selection methods for identifying predictor interactions in data with repeatedly measured binary outcomes.

Bethany J Wolf1, Yunyun Jiang2, Sylvia H Wilson3

  • 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.

Journal of Clinical and Translational Science
|May 5, 2021
PubMed
Summary
This summary is machine-generated.

New methods for selecting variables in clustered binary outcome data, like penalized and boosted approaches, are more effective than traditional stepwise selection. A two-stage method further improves parameter estimates, offering guidance for complex outcome analysis.

Keywords:
Variable selectionboostinginteractionspenalized regressiontwo-stage algorithm

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Health Outcomes Research

Background:

  • Analyzing longitudinal patient data requires modeling within-subject correlations.
  • Generalized linear mixed models (GLMMs) and generalized estimating equations (GEEs) handle these correlations.
  • Identifying variable interactions a priori can be challenging with these methods.

Purpose of the Study:

  • To evaluate variable selection methods for clustered binary outcomes.
  • To compare penalized and boosted approaches against traditional stepwise selection.
  • To assess a two-stage approach for improving parameter estimates.

Main Methods:

  • Simulations compared stepwise selection, penalized GLMM, boosted GLMM, and boosted GEE.
  • Variable selection considered main effects and two-way interactions.
  • A two-stage approach was evaluated to reduce bias and error.

Main Results:

  • Penalized and boosted methods outperformed stepwise selection in identifying predictors and interactions.
  • Boosted GLMM offered parsimony but was computationally intensive; Boosted GEE provided efficiency and parsimony.
  • The two-stage approach consistently reduced bias and error across all methods.

Conclusions:

  • Penalized and boosted methods are effective for variable selection in clustered binary data.
  • The two-stage approach is recommended to reduce bias and error.
  • Guidance is provided for selecting appropriate methods in practice.