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.

中文翻译：

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关于观察流行病学中变量的测量误差和错误分类的 STRATOS 指导文件：第 2 部分 - 更复杂的调整方法和高级主题。


我们继续审查流行病学中与测量误差和错误分类相关的问题。我们进一步描述了调整连续协变量测量误差引起的偏差估计的方法，包括似然法、贝叶斯方法、矩重构、矩调整插补和多重插补。然后我们描述哪些方法也可以用于分类协变量的错误分类。然后回顾了调整测量误差的连续变量分布估计的方法。这些部分提供了说明性示例。我们提供了用于实现这些方法的可用软件列表，并在支持信息中提供了用于实现示例的代码。接下来，我们提出几个高级主题，包括受经典误差和伯克森误差影响的数据、对具有测量误差的连续暴露进行建模、在同一模型中进行错误分类的分类暴露、当某些变量测量有误差时的变量选择、调整分析或设计结果变量中的误差，并对测量误差的连续变量进行分类。最后，我们为经常遇到的情况提供一些建议，即已知变量的测量存在很大误差，但只有外部参考标准或关于误差类型或大小的部分（或没有）信息。