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

Pamela A. Shaw, Paul Gustafson, Raymond J. Carroll, Veronika Deffner, Kevin W. Dodd, Ruth H. Keogh, Victor Kipnis, Janet A. Tooze, Michael P. Wallace, Helmut Küchenhoff, Laurence S. Freedman

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)2232-2263
Number of pages32
JournalStatistics in Medicine
Volume39
Issue number16
DOIs
StatePublished - 20 Jul 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
Published 2020. This article is a U.S. Government work and is in the public domain in the USA.

Funding

Natural Sciences and Engineering Research Council of Canada, RGPIN‐2019‐03957; Patient Centered Outcomes Research Institute (PCORI) Award, R‐1609‐36207; National Institutes of Health, NCI P30CA012197; U01‐CA057030; R01‐AI131771 Funding information This research is supported in part by the National Institutes of Health (NIH) grants R01‐AI131771 (P.A.S.), U01‐CA057030 (R.J.C.), NCI P30CA012197 (J.A.T.); Patient Centered Outcomes Research Institute (PCORI) Award R‐1609‐36207 (P.A.S.); and Natural Sciences and Engineering Research Council of Canada (NSERC) RGPIN‐2019‐03957 (P.G.). The statements in this manuscript are solely the responsibility of the authors and do not necessarily represent the views of NIH, PCORI, or NSERC.

FundersFunder number
National Institutes of HealthR01‐AI131771
National Cancer InstituteU01CA057030, P30CA012197, R‐1609‐36207
Natural Sciences and Engineering Research Council of CanadaRGPIN‐2019‐03957

    Keywords

    • Bayesian methods
    • bias analysis
    • distribution estimates
    • likelihood methods
    • moment reconstruction
    • multiple imputation

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