Brakenhoff Timo B, van Smeden Maarten, Visseren Frank L J, Groenwold Rolf H H
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands.
PLoS One. 2018 Feb 9;13(2):e0192298. doi: 10.1371/journal.pone.0192298. eCollection 2018.
With the increased use of data not originally recorded for research, such as routine care data (or 'big data'), measurement error is bound to become an increasingly relevant problem in medical research. A common view among medical researchers on the influence of random measurement error (i.e. classical measurement error) is that its presence leads to some degree of systematic underestimation of studied exposure-outcome relations (i.e. attenuation of the effect estimate). For the common situation where the analysis involves at least one exposure and one confounder, we demonstrate that the direction of effect of random measurement error on the estimated exposure-outcome relations can be difficult to anticipate. Using three example studies on cardiovascular risk factors, we illustrate that random measurement error in the exposure and/or confounder can lead to underestimation as well as overestimation of exposure-outcome relations. We therefore advise medical researchers to refrain from making claims about the direction of effect of measurement error in their manuscripts, unless the appropriate inferential tools are used to study or alleviate the impact of measurement error from the analysis.
随着越来越多地使用并非最初为研究而记录的数据，如常规护理数据（或“大数据”），测量误差必然会成为医学研究中一个越来越重要的问题。医学研究人员对随机测量误差（即经典测量误差）影响的普遍看法是，其存在会导致对所研究的暴露-结局关系产生某种程度的系统性低估（即效应估计值的衰减）。对于分析涉及至少一个暴露因素和一个混杂因素的常见情况，我们证明随机测量误差对估计的暴露-结局关系的影响方向可能难以预测。通过三项关于心血管危险因素的实例研究，我们表明暴露因素和/或混杂因素中的随机测量误差可能导致对暴露-结局关系的低估以及高估。因此，我们建议医学研究人员在其手稿中避免对测量误差的影响方向进行断言，除非使用适当的推断工具来研究或减轻分析中测量误差的影响。
