STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment.
Berkson error
SIMEX
classical error
differential error
measurement error
misclassification
nondifferential error
regression calibration
sample size
simulation extrapolation
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
20 07 2020
20 07 2020
Historique:
received:
09
12
2018
revised:
25
02
2020
accepted:
28
02
2020
pubmed:
5
4
2020
medline:
22
6
2021
entrez:
5
4
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32246539
doi: 10.1002/sim.8532
pmc: PMC7450672
mid: NIHMS1576756
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
2197-2231Subventions
Organisme : NCI NIH HHS
ID : U01 CA057030
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI131771
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA012197
Pays : United States
Organisme : Medical Research Council
ID : MR/M014827/1
Pays : United Kingdom
Organisme : NIAID NIH HHS
ID : R37 AI131771
Pays : United States
Organisme : Medical Research Council
ID : MR/S017968/1
Pays : United Kingdom
Informations de copyright
Published 2020. This article is a U.S. Government work and is in the public domain in the USA.
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