title: Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies.
abstract: BACKGROUND
  For chemicals with high within-subject temporal variability, assessing exposure biomarkers in a spot biospecimen poorly estimates average levels over long periods. The objective is to characterize the ability of within-subject pooling of biospecimens to reduce bias due to exposure misclassification when within-subject variability in biomarker concentrations is high.
  
  
  METHODS
  We considered chemicals with intraclass correlation coefficients of 0.6 and 0.2. In a simulation study, we hypothesized that the chemical urinary concentrations averaged over a given time period were associated with a health outcome and estimated the bias of studies assessing exposure that collected 1 to 50 random biospecimens per subject. We assumed a classical type error. We studied associations using a within-subject pooling approach and two measurement error models (simulation extrapolation and regression calibration), the latter requiring the assay of more than one biospecimen per subject.
  
  
  RESULTS
  For both continuous and binary outcomes, using one sample led to attenuation bias of 40% and 80% for compounds with intraclass correlation coefficients of 0.6 and 0.2, respectively. For a compound with an intraclass correlation coefficient of 0.6, the numbers of biospecimens required to limit bias to less than 10% were 6, 2, and 2 biospecimens with the pooling, simulation extrapolation, and regression calibration methods (these values were, respectively, 35, 8, and 2 for a compound with an intraclass correlation coefficient of 0.2). Compared with pooling, these methods did not improve power.
  
  
  CONCLUSIONS
  Within-subject pooling limits attenuation bias without increasing assay costs. Simulation extrapolation and regression calibration further limit bias, compared with the pooling approach, but increase assay costs.
authors.affiliation: From the aInserm, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, U1209 (IAB), Grenoble, France; bUniv. Grenoble-Alpes, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, IAB, Grenoble, France; cDepartment of Public Health Sciences, University of California, Davis, CA; and dMIND (Medical Investigations of Neurodevelopmental Disorders) Institute, University of California, Davis, CA.
authors.forename: Flavie
authors.initials: F
authors.lastname: Perrier
authors.forename: Lise
authors.initials: L
authors.lastname: Giorgis-Allemand
authors.forename: Rémy
authors.initials: R
authors.lastname: Slama
authors.forename: Claire
authors.initials: C
authors.lastname: Philippat
mesh_terms: D015982:Bias; D015415:Biomarkers / Q000652:urine*; D002138:Calibration; D003198:Computer Simulation; D006801:Humans; D012044:Regression Analysis; D013048:Specimen Handling; D014556:Urine / Q000737:chemistry*
chemical_list: D015415:Biomarkers