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

TL;DR

This review highlights that epidemiological studies often ignore measurement error, despite its known impact, and recommends improved data collection and analytical methods to address this pervasive issue.

Contribution

The paper reviews current practices across various epidemiological studies regarding measurement error and provides recommendations for better data collection and analysis methods.

Findings

Most studies acknowledge measurement error but rarely adjust for it.

Regression calibration is the most common adjustment method used.

There is a significant need for improved data collection and analytical approaches.

Abstract

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.