  Background: Variables in epidemiological observational studies are commonly
subject to measurement error and misclassification, but the impact of such
errors is frequently not appreciated or ignored. As part of the STRengthening
Analytical Thinking for Observational Studies (STRATOS) Initiative, a Task
Group on measurement error and misclassification (TG4) seeks to describe the
scope of this problem and the analysis methods currently in use to address
measurement error. Methods: TG4 conducted a literature survey of four types of
research studies that are typically impacted by exposure measurement error: 1)
dietary intake cohort studies, 2) dietary intake population surveys, 3)
physical activity cohort studies, and 4) air pollution cohort studies. The
survey was conducted to understand current practice for acknowledging and
addressing measurement error. Results: The survey revealed that while
researchers were generally aware that measurement error affected their studies,
very few adjusted their analysis for the error. Most articles provided
incomplete discussion of the potential effects of measurement error on their
results. Regression calibration was the most widely used method of adjustment.
Conclusions: Even in areas of epidemiology where measurement error is a known
problem, the dominant current practice is to ignore errors in analyses. Methods
to correct for measurement error are available but require additional data to
inform the error structure. There is a great need to incorporate such data
collection within study designs and improve the analytical approach. Increased
efforts by investigators, editors and reviewers are also needed to improve
presentation of research when data are subject to error.
