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

Shaw, PA; Gustafson, P; Carroll, RJ; Deffner, V; Dodd, KW; Keogh, RH; Kipnis, V; Tooze, JA; Wallace, MP; Küchenhoff, H; Freedman, LS

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

Shaw, PA Gustafson, P Carroll, RJ Deffner, V Dodd, KW Keogh, RH Kipnis, V Tooze, JA Wallace, MP Küchenhoff, H Freedman, LS

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Publisher:: Wiley
Publication Type:: Journal Article
Citation:: Statistics in medicine, 2020, 39, (16), pp. 2232-2263
Issue Date:: 2020-07

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Field	Value	Language
dc.contributor.author	Shaw, PA
dc.contributor.author	Gustafson, P
dc.contributor.author	Carroll, RJ
dc.contributor.author	Deffner, V
dc.contributor.author	Dodd, KW
dc.contributor.author	Keogh, RH
dc.contributor.author	Kipnis, V
dc.contributor.author	Tooze, JA
dc.contributor.author	Wallace, MP
dc.contributor.author	Küchenhoff, H
dc.contributor.author	Freedman, LS
dc.date.accessioned	2020-10-20T05:50:19Z
dc.date.available	2020-02-28
dc.date.available	2020-10-20T05:50:19Z
dc.date.issued	2020-07
dc.identifier.citation	Statistics in medicine, 2020, 39, (16), pp. 2232-2263
dc.identifier.issn	0277-6715
dc.identifier.issn	1097-0258
dc.identifier.uri	http://hdl.handle.net/10453/143407
dc.description.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.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Wiley
dc.relation.ispartof	Statistics in medicine
dc.relation.isbasedon	10.1002/sim.8531
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0104 Statistics, 1117 Public Health and Health Services
dc.subject.classification	Statistics & Probability
dc.title	STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2-More complex methods of adjustment and advanced topics.
dc.type	Journal Article
utslib.citation.volume	39
utslib.location.activity	England
utslib.for	0104 Statistics
utslib.for	1117 Public Health and Health Services
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-10-20T05:50:04Z
pubs.issue	16
pubs.publication-status	Published
pubs.volume	39
utslib.citation.issue	16

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/143407