How important is it that researchers account for variability between- and within-persons in exposure to help guide design of epidemiology studies (beyond just “thinking about it”)? (please explain).

Answer Explanations

• Very important

  Expert 3
  Between- and within-person exposure variability can result in exposure misclassification, which in turn attenuates the association between exposure and outcome. If not accounted for, this variability introduces noise into the data, weakening the study's conclusions. 
• Important

  Expert 2
  It is important for researchers to account for both between- and within-person variability in exposure when designing epidemiology studies, as it significantly impacts the accuracy and reliability of the study outcomes. Ignoring this variability can lead to exposure misclassification, resulting in biased estimates and misleading conclusions about exposure-response relationships. Properly addressing variability enhances statistical power by ensuring the study is neither under- nor overpowered, optimizing sample size and the number of measurements needed. Adjusting for within-person fluctuations helps capture true long-term associations, improving the precision of effect estimates and reducing bias. This approach is vital for deriving accurate, reliable conclusions that can inform public health interventions and policy decisions. By actively modeling variability rather than simply acknowledging it, researchers can better understand the true effects of exposure and design more valid, actionable studies.
  
  

  However, the importance of accounting for between- and within-person variability depends on the type of epidemiological study design. Epidemiology studies are diverse, and in some cases, it may be difficult or impractical to account for this variability. For example, cross-sectional or retrospective designs often rely on single-point or historical measurements, limiting the ability to capture temporal changes. Case-control studies based on historical data may also lack the flexibility to track exposure variability over time. While addressing variability is critical in many longitudinal or cohort studies, its consideration must be balanced against the practical constraints of the chosen study design.
• Important

  Expert 7
  It is important to think about both within and between variabilities in the design of the sutdies.
  More over, sample size calculations are often required to obtain funding.  How ever, such calculations rarely are true drivers of study design.  Usually, the sample siaze is driven by the availability of data and financial constrains.
• Very important

  Expert 1
  Obviously, this is dependent on the study design and aim in terms of biomonitoring of subjects. For many studies the most important exposure may be the comparison between subjects or within subjects. Therefore, the ability to accurately assess the variation either between or within subjects is critical to the success of the study. 
• Important

  Expert 9
  It is important to consider between- and within-person variability in exposure in order to increase the precision of results, and minimize exposure misclassification and thus biased estimates.  However, this importance can vary with study design and practical concerns.  Also, one of the benefits of using biomarkers of exposure is that many tend to integrate exposures over a period of time (which can vary with type of exposure, physiology, and sample medium); the resulting within-person variability may be lower than it would be by measuring exposure without using biomarkers. 
• Important

  Expert 8
  Important,  to evaluate if performing a study is worth it in the first place; and to determine sample size, choose appropriate measurement methods, whether repeated measurements or continuous monitoring are needed, etc. to be able to detect a hypothesized effect; and choose appropriate statistical methods to account for e.g. fixed and random effects, time-varying variables, etc.; and subsequently interpretation of results. 
• Very important

  Expert 5
  This must be considered in epidemiologic study design. Our students/researchers need more training and a better understanding of exposure measurement error to design and conduct studies that are sufficiently powered to detect risks that may not otherwise be found using traditional exposure measurements and epidemiologic approaches (i.e. yes/no, low/medium/high).