Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study’s inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given.

中文翻译：

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对现代方法的反思：流行病学研究中关于测量误差的五个神话。


流行病学家经常面临要分析的数据集，其中包含由于错误的数据输入、不准确的记录和测量仪器或程序错误而导致的测量误差。如果测量误差的影响判断错误，数据分析就会受到阻碍，研究推论的有效性可能会受到影响。在本文中，我们描述了导致测量误差误判的五个误区，涉及预期结构、影响和缓解误测量引起的问题的解决方案。目的是澄清这些测量误差的误解。我们表明，流行病学数据分析中测量误差的影响可以超越简单启发式的方式发挥作用，例如关于是否预期效应估计衰减的启发式。尽管我们鼓励流行病学家在分析中仔细考虑测量误差的结构和潜在影响，但我们也建议，如果不进行统计测量误差校正或定量偏差分析，在对测量误差影响的大小甚至方向做出声明时应保持克制。 。给出了缓解问题或调查测量误差的结构和大小的建议。