How important is it to calculate the impact of measurement error in effect modifiers in estimating power and bias of epidemiologic studies during planning stages?

Answer Explanations

• Very important

  Expert 3
   It depends on the size of the measurement error. For example, measurement errors of effect modifiers affect effect estimates. Without calculating the impact of measurement error, one cannot be certain of its influence. Therefore, it is necessary to calculate the measurement error in effect modifiers. 
• Equivocal

  Expert 6
  Consideration of effect modifiers (such as age, sex, or race) is more important, especially since these are usually measured with little or no error.  This is especially important if there are strong reasons to suspect effect modification by such factors.
• Not important

  Expert 7
  Effect modifiers are mostly unknown during planning, even more unknown would be a measurement error of effect modifiers.
• Important

  Expert 1
  This again would be important, to be honest, I don't think I've ever seen it done in a biomonitoring study. 
• Equivocal

  Expert 9
  It seems that assessing the impact of measurement error of effect modifiers could be helpful, but is probably not as important as measurement error of confounders.  Also, many of the effect modifiers commonly used in public health (e.g., age, sex, and to some extent race) are measured with little error.  However, some other effect modifiers that might be used (e.g. household income, urban/rural status) might have considerable error.
• Important

  Expert 8
  Depends on how important the effect modifier is + other factors in the study design. If e.g. differences by different subgroups are very large one will likely pick it up, also if there is some measurement error in the estimation of the effect modifier. If the modification is modest or the study has low power, one could miss potential effect modifiers, not pick them up in interaction analyses. 
  Also, when they are used for stratification, measurement error can lead to misclassification across strata.
  In general,  interaction analyses and stratified analyses require larger sample sizes.
  Overall: somewhat important but less than measurement error in exposures and often probably also a bit less important than measurement error in confounders and outcomes. 
• Important

  Expert 5
  For effect modifiers, my answer to this question is  similar to my answer re: confounders above.  
  Theoretically, I believe that error in effect modifiers may result in bias in more significant ways as analyses are often stratified based on a level of an effect modifier.  If one level has low numbers, this can result in a significant lack of power for estimating the interaction effects.  Generally, the greatest power is achieved when the prevalence of dichotomous effect modifier is 50%.
• Important

  Expert 4
  As above, very important to have a sense of the impact, but sometimes it is difficult to quantify the impact due to lack of knowledge on the exact measurement error in the study population.
• Important

  Expert 2
  In my opinion, it's also important to account for measurement error in effect modifiers during the planning stages of epidemiologic studies. Effect modifiers can influence the relationship between exposure and outcome, and errors in their measurement can obscure interactions, leading to biased estimates and incorrect conclusions about subgroup effects. Additionally, this type of error can reduce the power to detect true interactions. Addressing this upfront helps design a study that better estimates interaction effects and minimizes the risk of false positives or negatives.
  However, when comparing measurement error in confounders and effect modifiers, I believe measurement error in confounders is more critical. Confounders, if mismeasured, can directly bias the overall exposure-outcome estimate, potentially leading to spurious or masked associations. Even small errors in confounders can significantly distort results, whereas effect modifiers primarily affect subgroup or interaction analysis without directly biasing the main exposure-outcome relationship.
  Overall, while both are important, I prioritize addressing measurement error in confounders, as it has a more direct and widespread impact on study validity. Measurement error in effect modifiers, while important for subgroup analysis, has a more nuanced effect on overall conclusions.