STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1—Basic theory and simple methods of adjustment

• Published in: Statistics in medicine Vol. 39; no. 16; pp. 2197 - 2231
• Main Authors: Keogh, Ruth H., Shaw, Pamela A., Gustafson, Paul, Carroll, Raymond J., Deffner, Veronika, Dodd, Kevin W., Küchenhoff, Helmut, Tooze, Janet A., Wallace, Michael P., Kipnis, Victor, Freedman, Laurence S.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 20.07.2020; Wiley Subscription Services, Inc
• Subjects: Berkson error; Bias; Calibration; Causality; classical error; Computer Simulation; differential error; Epidemiology; Humans; measurement error; Measurement errors; Medical statistics; misclassification; Models, Statistical; nondifferential error; regression calibration; Research Design; sample size; SIMEX; simulation extrapolation; Variables; differential error; measurement error; regression calibration; simulation extrapolation; classical error; SIMEX; Berkson error; sample size; nondifferential error; misclassification
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.8532

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.