Epidemiologic analyses with error-prone exposures: review of current practice and recommendations.

Author(s): Shaw PA,  Deffner V,  Keogh RH,  Tooze JA,  Dodd KW,  Küchenhoff H,  Kipnis V,  Freedman LS,  Measurement Error and Misclassification Topic Group (TG4) of the STRATOS Initiative

Journal: Ann Epidemiol

Date: 2018 Nov

Major Program(s) or Research Group(s): BRG

PubMed ID: 30316629

PMC ID: PMC6734186

Abstract: PURPOSE: Variables in observational studies are commonly subject to measurement error, but the impact of such errors is frequently ignored. As part of the STRengthening Analytical Thinking for Observational Studies Initiative, a task group on measurement error and misclassification seeks to describe the current practice for acknowledging and addressing measurement error. METHODS: Task group on measurement error and misclassification 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. 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: Methods to correct for measurement error are available but require additional data regarding 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 needed to improve presentation of research when data are subject to error.