Shaw Pamela A, Gustafson Paul, Carroll Raymond J, Deffner Veronika, Dodd Kevin W, Keogh Ruth H, Kipnis Victor, Tooze Janet A, Wallace Michael P, Küchenhoff Helmut, Freedman Laurence S
Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada.
Stat Med. 2020 Jul 20;39(16):2232-2263. doi: 10.1002/sim.8531. Epub 2020 Apr 3.
We continue our review of issues related to measurement error and misclassification in epidemiology. We further describe methods of adjusting for biased estimation caused by measurement error in continuous covariates, covering likelihood methods, Bayesian methods, moment reconstruction, moment-adjusted imputation, and multiple imputation. We then describe which methods can also be used with misclassification of categorical covariates. Methods of adjusting estimation of distributions of continuous variables for measurement error are then reviewed. Illustrative examples are provided throughout these sections. We provide lists of available software for implementing these methods and also provide the code for implementing our examples in the Supporting Information. Next, we present several advanced topics, including data subject to both classical and Berkson error, modeling continuous exposures with measurement error, and categorical exposures with misclassification in the same model, variable selection when some of the variables are measured with error, adjusting analyses or design for error in an outcome variable, and categorizing continuous variables measured with error. Finally, we provide some advice for the often met situations where variables are known to be measured with substantial error, but there is only an external reference standard or partial (or no) information about the type or magnitude of the error.
我们继续回顾流行病学中与测量误差和错误分类相关的问题。我们进一步描述了针对连续协变量测量误差导致的有偏估计进行调整的方法，涵盖似然方法、贝叶斯方法、矩重建、矩调整插补和多重插补。然后我们描述了哪些方法也可用于分类协变量的错误分类。接着回顾了针对测量误差调整连续变量分布估计的方法。在这些章节中都提供了示例。我们列出了可用于实现这些方法的软件，并在补充信息中提供了实现我们示例的代码。接下来，我们介绍几个高级主题，包括同时受经典误差和伯克森误差影响的数据、对存在测量误差的连续暴露进行建模、在同一模型中对存在错误分类的分类暴露进行建模、当一些变量存在测量误差时的变量选择、针对结果变量中的误差调整分析或设计以及对存在测量误差的连续变量进行分类。最后，对于变量已知存在大量测量误差，但仅存在外部参考标准或关于误差类型或大小的部分（或无）信息这种经常遇到的情况，我们提供了一些建议。
