How important is it to calculate the impact of measurement error in the outcome measure in estimating power and bias of epidemiologic studies during planning stages? (please explain)

Answer Explanations

• Very important

  Expert 3
   It depends on the effect size of the measurement error in the outcome measure.  Without calculating the impact of measurement error, one cannot be certain of its influence. Therefore, it is necessary to calculate the measurement error in the outcomes. 
• Equivocal

  Expert 7
  Measurement error in the outcome should be minimized at the outset.
• Important

  Expert 1
  The measurement error should be minimized so as to provide greater validity to the outcome.
• Very important

  Expert 9
  Measurement error in the outcome must be considered when designing a study.  First, the researcher should try to take all feasible steps to minimize measurement error in the outcome through study design, data collection techniques, etc.  But some measurement error will inevitably remain, and this should be taken into account in estimating power and bias.  
• Important

  Expert 8
  Important but i.m.o somewhat less than errors in exposure assessment
• Important

  Expert 5
  The inclusion/exclusion criteria should be clear and properly classify individuals into groups.  If a continuous outcome is measured, it is important to understand the relationship between the measured outcome and the true outcome.  Sample sizes can be increased and/or repeated measurements within subjects over time can improve study power. Also, statistical adjustments and/or sensitivity analyses can be performed. 
• Very important

  Expert 4
  I believe any researchers will minimise the measurement error in the outcome measure, but still if error exists it should be estimated.
• Very important

  Expert 2
  I believe it's very important to calculate the impact of measurement error in the outcome measure during the planning stages of an epidemiologic study. Ignoring measurement error in the outcome can lead to biased effect estimates. When outcome measurement error is underestimated, it can reduce variability and exaggerate the association between exposure and outcome. This increases the risk of false positives and can significantly undermine the validity of the study’s conclusions, particularly when the sample size is small.
  By accounting for this error during the planning phase, we can make necessary adjustments to the study design—such as increasing the sample size or improving the accuracy of the outcome measure—to ensure the results are both reliable and unbiased.  For this reason, I recommend placing emphasis on the impact of measurement error in the outcome measure when planning a study.