How useful are the calculators for estimating the power of epidemiological studies under varying levels of measurement error and sample sizes in the presence of classical measurement error in exposure?

Answer Explanations

• Somewhat useful

  Expert 9
  Usefulness likely varies with the exposure(s) and the available data on variability associated with them.
• Somewhat useful

  Expert 3
   The calculators seem more like a textbook, where individual aspects are presented separately for understanding. The properties of the calculators appear to be interrelated, and I would expect a single calculator that combines all these properties for a more comprehensive analysis. 
• Somewhat useful

  Expert 4
  It is useful at study design stage to maximise the statistical power by different combinations of repeated measurements and sample size. 
• Somewhat useful

  Expert 6
  They would be more useful if they did a better job of explaining the units of the component parameters, such as the mean, standard deviation, variance, desired margin of error, and minimal detectable effect.  Most of these parameters are expressed as standardized (SD) units, which may not be easily interpretable by readers.
• Somewhat useful

  Expert 7
  Calculators are useful, however their use

  requires lots of info not usually available.
  What if ICC is unknown?
• Extremely useful

  Expert 1
  Being able to see the differences in Power, Sample Size and Measurement error allow the scientist great flexibility to see given the limitations of funding, sample size, the exact values for Power that can be reached.
• Somewhat useful

  Expert 8
  Definitely useful, as always, simple easy to use calculators come with a trade-off of flexibility in making changes by the researcher. But those that have ample experience will likely do them themselves or know how to handle their specific data and assumptions. For those that are less familiar and would otherwise maybe not calculate power taking into account measurement errors this will be extremely helpful. 
• Somewhat useful

  Expert 5
  I think these are most useful for individuals who typically don't do sample size or power calculations incorporating measures of within-individual variation or exposure measurement error.  
  In my area of research (occupational/environmental epi), Epidemiologists/Statisticians would typically write their own programs (in SAS or R) to estimate sample sizes and tailor them to their specific study. 
• Somewhat useful

  Expert 2
   While the calculators presented in the white paper offer valuable insights and tools for optimizing study design in the presence of classical measurement error, I would characterize them as "somewhat useful." This is because the calculators focus on basic and classic scenarios, such as additive, normally distributed measurement errors, which may not fully capture the complexities of many real-world epidemiological studies. For instance, they do not account for the presence of confounders, which are critical in most epidemiological analyses. Moreover, the calculators do not consider different study designs, such as cohort studies, case-control studies, or time-to-event analyses, which have unique statistical requirements and challenges. Without these considerations, the applicability of the calculators is limited to a narrower set of circumstances, making them somewhat useful but not universally applicable for all epidemiological research contexts. 
  Nevertheless, the calculators offer a convenient and free alternative to more expensive commercial software for sample size and power determination, such as PASS, and they help avoid the need for advanced programming in statistical software like SAS or Stata.