Statistical Methods for Modeling Exposure Variables Subject to Limit of Detection

Details

• Personal Author:
  Ghassabian A ; Liu M ; Seok E ; Wang Y
• Description:
  Environmental health research aims to assess the impact of environmental exposures, making it crucial to understand their effects due to their broad impacts on the general population. However, a common issue with measuring exposures using bio-samples in laboratory is that values below the limit of detection (LOD) are either left unreported or inaccurately read by machines, which subsequently influences the analysis and assessment of exposure effects on health outcomes. We address the challenge of handling exposure variables subject to LOD when they are treated as either covariates or an outcome. We evaluate the performance of commonly-used methods including complete-case analysis and fill-in method, and advanced techniques such as multiple imputation, missing-indicator model, two-part model, Tobit model, and several others. We compare these methods through simulations and a dataset from NHANES 2013-2014. Our numerical studies show that the missing-indicator model generally yields reasonable estimates when considering exposure variables as covariates under various settings, while other methods tend to be sensitive to the LOD-missing proportions and/or distributional skewness of exposures. When modeling an exposure variable as the outcome, Tobit model performs well under Gaussian distribution and quantile regression generally provides robust estimates across various shapes of the outcome's distribution. In the presence of missing data due to LOD, different statistical models should be considered for being aligned with scientific questions, model assumptions, requirements of data distributions, as well as their interpretations. Sensitivity analysis to handle LOD-missing exposures can improve the robustness of model conclusions. [Description provided by NIOSH]
• Subjects:
  Environmental Exposure Occupational Health Safety
• Keywords:
  Author Keywords: Environmental Exposure Environmental Exposure Exposure Assessment Limit Of Detection LOD Mathematical Models Missing Data Multiple Imputation NHANES Statistical Analysis Statistical Quality Control Tobit Model Two-part Model

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• ISSN:
  1867-1764
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  New York ; OSHA Region 2
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  435-458
• Volume:
  16
• Issue:
  2
• NIOSHTIC Number:
  nn:20068906
• Citation:
  Stat Biosci 2024 Jul; 16(2):435-458
• Contact Point Address:
  Mengling Liu, Department of Population Health, New York University Grossman School of Medicine, New York, NY, 10016, USA
• Email:
  mengling.liu@nyulangone.org
• Federal Fiscal Year:
  2024
• Performing Organization:
  New York University School of Medicine
• Peer Reviewed:
  True
• Start Date:
  20230701
• Source Full Name:
  Statistics in Biosciences
• End Date:
  20260630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:b909ad10d6c3d6adbce373e02b0ee0a47a79c696fad6d704cb2bbd79c82ba6a3ba742c7a656c38df1e4aa9e25d22be8b3f2f282912143a73c6d439066380e9d8
• Download URL:
  https://stacks.cdc.gov/view/cdc/207469/cdc_207469_DS1.pdf
• File Type:
  [PDF - 1.51 MB ]