Epidemiol Rev. 2022 Jan 14;43(1):94-105. doi: 10.1093/epirev/mxab011.
Measurement error, although ubiquitous, is uncommonly acknowledged and rarely assessed or corrected in epidemiologic studies. This review offers a straightforward guide to common problems caused by measurement error in research studies and a review of several accessible bias-correction methods for epidemiologists and data analysts. Although most correction methods require criterion validation including a gold standard, there are also ways to evaluate the impact of measurement error and potentially correct for it without such data. Technical difficulty ranges from simple algebra to more complex algorithms that require expertise, fine tuning, and computational power. However, at all skill levels, software packages and methods are available and can be used to understand the threat to inferences that arises from imperfect measurements.
测量误差虽然普遍存在，但在流行病学研究中却很少被承认和评估，更遑论纠正。本综述为研究中因测量误差而产生的常见问题提供了一个简单明了的指南，并回顾了几种可供流行病学家和数据分析人员使用的易于访问的偏差校正方法。尽管大多数校正方法都需要包括金标准在内的标准验证，但也有一些无需此类数据即可评估测量误差影响并可能进行校正的方法。技术难度从简单的代数学到更复杂的需要专业知识、微调以及计算能力的算法不等。然而，在各个技术水平上，都有软件包和方法可供使用，以帮助理解因不完善测量而对推论产生的威胁。
