Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods

Details

• Personal Author:
  Batterman S ; Beesley LJ ; Boss J ; Du J ; Feldman EL ; Goutman SA ; Han P ; Kleinsasser M ; Mukherjee B
• Description:
  Penalized regression methods are used in many biomedical applications for variable selection and simultaneous coefficient estimation. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors. This article considers a general class of penalized objective functions which, by construction, force selection of the same variables across imputed datasets. By pooling objective functions across imputations, optimization is then performed jointly over all imputed datasets rather than separately for each dataset. We consider two objective function formulations that exist in the literature, which we will refer to as "stacked" and "grouped" objective functions. Building on existing work, we (i) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (ii) incorporate adaptive shrinkage penalties, (iii) compare these methods through simulation, and (iv) develop an R package miselect. Simulations demonstrate that the "stacked" approaches are more computationally efficient and have better estimation and selection properties. We apply these methods to data from the University of Michigan ALS Patients Biorepository aiming to identify the association between environmental pollutants and ALS risk. Supplementary materials for this article are available online. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety
• Keywords:
  Author Keywords: Elastic Net Data Processing Group LASSO Majorization-minimization Missing Data Multiple Imputation Pooled Objective Function Regression Analysis Statistical Analysis Statistical Quality Control
• ISSN:
  1061-8600
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Michigan ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  31
• Issue:
  4
• NIOSHTIC Number:
  nn:20070717
• Citation:
  J Comput Graph Stat 2022 Oct; 31(4):1063-1075
• Contact Point Address:
  Bhramar Mukherjee, Department of Biostatistics, University of Michigan, Ann Arbor, MI
• Email:
  bhramar@umich.edu
• Federal Fiscal Year:
  2023
• Performing Organization:
  University of Michigan, Ann Arbor
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Journal of Computational and Graphical Statistics
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:e2f4aa5f15a2f762f5cd647ec6b4d1f06b3f9978e14925e4dd2a157fcabfe5d8e05ef34d5278b77af42112b958fc785a3c1c1f4aa8c4a79a54a174031716c5f7
• Download URL:
  https://stacks.cdc.gov/view/cdc/208889/cdc_208889_DS1.pdf
• File Type:
  [PDF - 2.37 MB ]