STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2—More complex methods of adjustment and advanced topics

• Published in: Statistics in medicine Vol. 39; no. 16; pp. 2232 - 2263
• Main Authors: 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.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 20.07.2020; Wiley Subscription Services, Inc
• Subjects: Bayes Theorem; Bayesian analysis; Bayesian methods; Bias; bias analysis; distribution estimates; Epidemiology; Humans; likelihood methods; Measurement errors; Medical statistics; moment reconstruction; multiple imputation; Variables; multiple imputation; distribution estimates; likelihood methods; moment reconstruction; bias analysis; Bayesian methods
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.8531

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.