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

Details

• Alternative Title:
  J Expo Sci Environ Epidemiol
• Personal Author:
  Chen, I-Chen ; Bertke, Stephen J. ; 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 modelling the geometric mean operate under the assumption the data is 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. Additionally, 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.
  
  
• Subjects:
  Article Bias Computer Simulation Humans Left Censoring Limit Of Detection Models, Statistical Occupational Exposure Quantile Regression Models Repeated Measures Right Skewness

  More +
• Source:
  J Expo Sci Environ Epidemiol. 31(6):1057-1066
• Pubmed ID:
  34108633
• Pubmed Central ID:
  PMC8595850
• Document Type:
  Journal Article
• Funding:
  CC999999/ImCDC/Intramural CDC HHSUnited States/
• Volume:
  31
• Issue:
  6
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:dcedd4da7b8ccc05df99f42bb3bcf398c2d3b1232b2dd3bbc3a37874f335672f
• Download URL:
  https://stacks.cdc.gov/view/cdc/111863/cdc_111863_DS1.pdf
• File Type:
  [PDF - 477.16 KB ]