Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia.
Department of Statistics, Statistical Consulting Unit StaBLab, Ludwig-Maximilians- Universität, Munich, Germany.
Ann Epidemiol. 2018 Nov;28(11):821-828. doi: 10.1016/j.annepidem.2018.09.001. Epub 2018 Sep 18.
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.
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.
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.
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.
观察性研究中的变量通常受到测量误差的影响，但这些误差的影响经常被忽视。作为加强观察性研究分析思维倡议的一部分，一个关于测量误差和分类错误的工作组旨在描述目前承认和解决测量误差的实践。
测量误差和分类错误工作组对四种通常受暴露测量误差影响的研究进行了文献调查：（1）饮食摄入队列研究，（2）饮食摄入人群调查，（3）体力活动队列研究，和（4）空气污染队列研究。
调查显示，尽管研究人员普遍意识到测量误差会影响他们的研究，但很少有人对分析进行调整以纠正误差。大多数文章对测量误差对结果的潜在影响的讨论不完整。回归校准是最广泛使用的调整方法。
虽然有纠正测量误差的方法，但需要更多关于误差结构的额外数据。非常需要在研究设计中纳入此类数据收集，并改进分析方法。研究人员、编辑和评论员需要加大努力，在数据存在误差时改进研究的呈现方式。
