How would you characterize your familiarity with the magnitudes of between- and within-person variability in exposure biomarkers?

Answer Explanations

• Somewhat familiar

  Expert 8
  I've worked with various biomarkers, but mostly limited to nutritional exposure assessment
• Somewhat familiar

  Expert 3
  I am familiar with the magnitudes of between- and within-person variability for a few selected exposures, such as manganese, TCE, HCHO, and PAHs. 
• Very familiar

  Expert 5
  I've taught courses in exposure measurement error and effects on power and bias etc. in PhD level and other groups.   I've studied this in field settings (pesticide exposure) using urine samples (spot samples, 24 hour etc.), and also studied the effects of urine dilution correction (SG and creatinine).
• Very familiar

  Expert 4
  Have been working in a very similar topic for several years, which is about measurements of habitual physical activity level and sleep. 
• Very familiar

  Expert 6
  I am more familiar with clustering (within-person correlation) of outcomes in cluster-randomized trials and with clustered measurements due to small numbers of observers who measure the outcomes, but the statistical principles are the same.
• Somewhat familiar

  Expert 7
  Non-persistent chemicals generally exhibit higher within-person variability, while persistent chemicals tend to show greater between-person variability.
• Somewhat familiar

  Expert 1
  Variability of between person and within person is commonly measured in many types of epidemiological studies, including meta-analysis which compares the between study and within study variability. The magnitude of the variability is important in determining the the stability of the biomarker being measured over time.
• Not very familiar

  Expert 9
  I'm familiar with the concept, but not the actual values of between- and within-person variability in exposure biomarkers.  Plus of course, the actual values would vary between different exposure compounds, their half-lives and metabolic products, and sampling media (e.g. blood, urine, saliva).
• Somewhat familiar

  Expert 2
  I have been responsible for or involved in numerous environmental epidemiology studies focused on environmental risk factors, including heavy metals, pesticides, and air pollutants. Some of these studies utilized longitudinal designs with repeated measurements of exposures. In these studies, we employed advanced statistical techniques, such as mixed-effects models and Generalized Estimating Equations (GEE), to account for both between- and within-person variability as well as clustering effects in exposure data.