Details:

• Alternative Title:
  J Expo Sci Environ Epidemiol
• Personal Author:
  Chen, I-Chen ; Bertke, Stephen J. ; Estill, Cheryl Fairfield

  Chen, I-Chen ; Bertke, Stephen J. ; Estill, Cheryl Fairfield Less -
• Description:
  Background
  
  Environmental exposure and biomonitoring data with repeated measurements from environmental and occupational studies are commonly right-skewed and in the presence of limits of detection (LOD). However, existing model has not been discussed for small-sample properties and highly skewed data with non-detects and repeated measurements.
  
  Objective
  
  Marginal modeling provides an alternative to analyzing longitudinal and cluster data, in which the parameter interpretations are with respect to marginal or population-averaged means.
  
  Methods
  
  We outlined the theories of three marginal models, i.e., generalized estimating equations (GEE), quadratic inference functions (QIF), and generalized method of moments (GMM). With these approaches, we proposed to incorporate the fill-in methods, including single and multiple value imputation techniques, such that any measurements less than the limit of detection are assigned values.
  
  Results
  
  We demonstrated that the GEE method works well in terms of estimating the regression parameters in small sample sizes, while the QIF and GMM outperform in large-sample settings, as parameter estimates are consistent and have relatively smaller mean squared error. No specific fill-in method can be deemed superior as each has its own merits.
  
  Significance
  
  The marginal models for environmental exposure and biomonitoring data with repeated measurements and non-detects performed validity of statistical inference, especially in small-sample settings. In addition to the use of mixed effects models, these models will broaden and benefit the repeated measures data analysis in the field of exposure science.
  
  Impact Statement
  
  Marginal modeling is firstly employed to analyze repeated measures data with non-detects, in which only the mean structure needs to be correctly provided to obtain consistent parameter estimates. After replacing non-detects through substitution methods and utilizing small-sample bias corrections, in a simulation study we found that the estimating approaches used in the marginal models have corresponding advantages under a wide range of sample sizes. We also applied the models to longitudinal and cluster working examples.
  
  
• Subjects:
  [+]

  Biological Monitoring Environmental Exposure Environmental Monitoring Humans Limit Of Detection Models, Statistical
• Keywords:
  [+]

  Environmental Exposure Left Censoring Limit Of Detection Marginal Analysis Repeated Measures Right Skewness
• Source:
  J Expo Sci Environ Epidemiol. 34(6):1018-1027
• Pubmed ID:
  38253592
• Pubmed Central ID:
  PMC11260897
• Document Type:
  Journal Article
• Funding:
  CC999999/ImCDC/Intramural CDC HHSUnited States/
• Volume:
  34
• Issue:
  6
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  [+]

  urn:sha-512:84d6de94030d24476de465f82aff7c5f33aa056bc08e6db63fdd5dab9f2ed9d532e082f04c5354da8d84c83e5b3c0fe80045c5cb7f181542a2a2b0d5b56feb74
• File Type:

• Supporting Files:
  No Additional Files