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Variable selection with multiply-imputed datasets: choosing between stacked and grouped methods.

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  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI.

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|January 16, 2023
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Summary
This summary is machine-generated.

This study introduces penalized regression methods that consistently select variables across multiple imputed datasets, overcoming challenges posed by missing data in biomedical research. The "stacked" approach offers improved computational efficiency and accuracy for variable selection.

Keywords:
Elastic NetGroup LASSOMajorization-MinimizationMissing DataMultiple ImputationPooled Objective Function

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Area of Science:

  • Biomedical data analysis
  • Statistical modeling
  • Machine learning in healthcare

Background:

  • Penalized regression is crucial for variable selection and coefficient estimation in biomedical studies.
  • Missing data and multiple imputation complicate standard penalized regression implementations, often leading to inconsistent variable selection across imputed datasets.

Purpose of the Study:

  • To develop penalized regression methods that ensure consistent variable selection across multiple imputed datasets.
  • To introduce novel objective function formulations ('stacked' and 'grouped') for joint optimization over imputed data.
  • To implement efficient optimization algorithms and an R package for practical application.

Main Methods:

  • Proposed a general class of penalized objective functions designed to enforce consistent variable selection across imputations.
  • Developed and implemented efficient cyclic coordinate descent and majorization-minimization algorithms for continuous and binary outcomes.
  • Incorporated adaptive shrinkage penalties and compared 'stacked' versus 'grouped' objective functions via simulation studies.

Main Results:

  • Simulations indicated that the 'stacked' objective function approach demonstrated superior computational efficiency, estimation accuracy, and variable selection performance compared to the 'grouped' approach.
  • The developed methods were applied to the University of Michigan ALS Patients Biorepository dataset to investigate associations between environmental pollutants and Amyotrophic Lateral Sclerosis (ALS) risk.

Conclusions:

  • The proposed penalized regression framework effectively addresses the challenge of variable selection with missing data handled by multiple imputation.
  • The 'stacked' objective function and associated algorithms provide a robust and efficient tool for biomedical researchers, as demonstrated by the ALS data application.
  • An R package, `miselect`, has been developed to facilitate the implementation of these advanced statistical methods.