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STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment.
Statistics in Medicine ( IF 2 ) Pub Date : 2020-04-03 , DOI: 10.1002/sim.8532
Ruth H Keogh 1 , Pamela A Shaw 2 , Paul Gustafson 3 , Raymond J Carroll 4, 5 , Veronika Deffner 6 , Kevin W Dodd 7 , Helmut Küchenhoff 8 , Janet A Tooze 9 , Michael P Wallace 10 , Victor Kipnis 7 , Laurence S Freedman 11, 12
Affiliation  

Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear, and Berkson models, and on the concepts of nondifferential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the article deals with more advanced topics.

中文翻译:

STRATOS 关于观察流行病学中的测量误差和变量错误分类的指导文件:第 1 部分 - 基本理论和简单的调整方法。

流行病学中经常发生变量的测量误差和错误分类,并且涉及对公共卫生重要的变量。它们的存在会对涉及这些变量的统计分析结果产生强烈影响。然而,调查人员通常不会注意到这种错误测量造成的偏差。我们分两部分概述了发生的错误类型、它们对分析结果的影响以及减轻它们造成的偏差的统计方法。在第一部分中,我们回顾了不同类型的测量误差和错误分类,强调经典模型、线性模型和伯克森模型,以及非微分误差和微分误差的概念。我们描述了协变量和结果变量中这些类型的误差对各种分析的影响,包括回归模型中的估计和测试以及估计分布。我们概述了提供此类误差信息所需的辅助研究类型,并讨论了协变量测量误差对研究设计的影响。概述了确定样本量要求的方法,既适用于旨在提供有关测量误差信息的辅助研究,也适用于测量有误差的感兴趣暴露的主要研究。我们描述了两种更简单的方法,回归校准和模拟外推(SIMEX),它们调整由连续协变量的测量误差引起的回归系数的偏差,并通过从观察蛋白质和能量(OPEN)饮食验证中提取的示例说明它们的使用学习。最后,我们回顾了可用于实施这些方法的软件。本文的第二部分涉及更高级的主题。
更新日期:2020-04-03
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