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Gregory Vaughan1, Robert Aseltine2,3, Kun Chen2,4

  • 1Department of Mathematical Sciences, Bentley University, Waltham, Massachusetts, USA.

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This summary is machine-generated.

This study introduces two novel methods for selecting statistical models with interaction terms, ensuring hierarchical structure. These approaches improve model accuracy and computational efficiency for clustered data analysis.

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Model selection with interaction terms requires maintaining hierarchy between main effects and interactions.
  • Generalized estimating equations are used for clustered data analysis.
  • Existing methods may struggle with hierarchical structure in models containing interaction terms.

Purpose of the Study:

  • To present two stagewise estimation approaches for selecting models with interaction terms.
  • To ensure the hierarchy between main effects and interaction terms is maintained.
  • To apply these methods to analyze suicide attempt hospitalization rates in adolescents.

Main Methods:

  • Developed a hierarchical lasso stagewise estimating equations approach.
  • Developed a stagewise active set approach enforcing variable hierarchy.
  • Utilized generalized estimating equations for clustered data.
  • Assessed effectiveness through simulation studies.

Main Results:

  • The proposed techniques effectively select interaction terms while maintaining hierarchy.
  • Both methods demonstrated superior computational efficiency compared to existing approaches.
  • Simulation studies validated the effectiveness of the new methods.

Conclusions:

  • The novel stagewise estimation approaches provide effective and efficient model selection for interaction terms in clustered data.
  • These methods offer a robust solution for complex statistical modeling, particularly in public health research.
  • The application to suicide attempt hospitalization rates highlights the practical utility of the developed techniques.