Quantile Regression for Exposure Data with Repeated Measures in the Presence of Non-Detects

Details

• Personal Author:
  Bertke SJ ; Chen I-C ; Curwin, Brian D.
• Description:
  Background: Exposure data with repeated measures from occupational studies are frequently right-skewed and left-censored. To address right-skewed data, data are generally log-transformed and analyses modeling the geometric mean operate under the assumption the data are log-normally distributed. However, modeling the mean of exposure may lead to bias and loss of efficiency if the transformed data do not follow a known distribution. In addition, left censoring occurs when measurements are below the limit of detection (LOD). Objective: To present a complete illustration of the entire conditional distribution of an exposure outcome by examining different quantiles, rather than modeling the mean. Methods: We propose an approach combining the quantile regression model, which does not require any specified error distributions, with the substitution method for skewed data with repeated measurements and non-detects. Results: In a simulation study and application example, we demonstrate that this method performs well, particularly for highly right-skewed data, as parameter estimates are consistent and have smaller mean squared error relative to existing approaches. Significance: The proposed approach provides an alternative insight into the conditional distribution of an exposure outcome for repeated measures models. [Description provided by NIOSH]
• Subjects:
  Environmental Exposure Occupational Exposure Occupational Health Safety
• Keywords:
  Author Keywords: Left Censoring Exposure Levels Limit Of Detection Measurement Equipment Models Occupational Exposure Quantile Regression Model Repeated Measures Right Skewness
• ISSN:
  1559-0631
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  Ohio ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  DFSE - Division of Field Studies and Engineering
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  31
• Issue:
  6
• NIOSHTIC Number:
  nn:20062936
• Citation:
  J Expo Sci Environ Epidemiol 2021 Nov; 31(6):1057-1066
• Contact Point Address:
  I-Chen Chen, Division of Field Studies and Engineering, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, OH 45226, USA
• Email:
  okv0@cdc.gov
• Federal Fiscal Year:
  2022
• Peer Reviewed:
  True
• Source Full Name:
  Journal of Exposure Science and Environmental Epidemiology
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:479ef0a5d173786045c1b8729065ffd4edfaed3a4eadfe55e15f4caa5db165ddb80ca24ae59fd60e373c6fcd08dac51cb8bd420817db6f8b72e80aeab7844f7a
• Download URL:
  https://stacks.cdc.gov/view/cdc/226099/cdc_226099_DS1.pdf
• File Type:
  [PDF - 738.51 KB ]