Literature DB >> 1746532

Differential misclassification arising from nondifferential errors in exposure measurement.

K M Flegal1, P M Keyl, F J Nieto.   

Abstract

Misclassification into exposure categories formed from a continuous variable arises from measurement error in the continuous variable. Examples and mathematical results are presented to show that if the measurement error is nondifferential (independent of disease status), the resulting misclassification will often be differential, even in cohort studies. The degree and direction of differential misclassification vary with the exposure distribution, the category definitions, the measurement error distribution, and the exposure-disease relation. Failure to recognize the likelihood of differential misclassification may lead to incorrect conclusions about the effects of measurement error on estimates of relative risk when categories are formed from continuous variables, such as dietary intake. Simulations were used to examine some effects of nondifferential measurement error. Under the conditions used, nondifferential measurement error reduced relative risk estimates, but not to the degree predicted by the assumption of nondifferential misclassification. When relative risk estimates were corrected using methods appropriate for nondifferential misclassification, the "corrected" relative risks were almost always higher than the true relative risks, sometimes considerably higher. The greater the measurement error, the more inaccurate was the correction. The effects of exposure measurement errors need more critical evaluation.

Mesh:

Year:  1991        PMID: 1746532     DOI: 10.1093/oxfordjournals.aje.a116026

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  60 in total

Review 1.  Smoothing in occupational cohort studies: an illustration based on penalised splines.

Authors:  E A Eisen; I Agalliu; S W Thurston; B A Coull; H Checkoway
Journal:  Occup Environ Med       Date:  2004-10       Impact factor: 4.402

2.  The impact of exposure categorisation for grouped analyses of cohort data.

Authors:  D B Richardson; D Loomis
Journal:  Occup Environ Med       Date:  2004-11       Impact factor: 4.402

3.  Suicide incidence and risk factors in an active duty US military population.

Authors:  Jeffrey Hyman; Robert Ireland; Lucinda Frost; Linda Cottrell
Journal:  Am J Public Health       Date:  2012-01-25       Impact factor: 9.308

4.  Poisson regression analysis of ungrouped data.

Authors:  D Loomis; D B Richardson; L Elliott
Journal:  Occup Environ Med       Date:  2005-05       Impact factor: 4.402

5.  Exposure-measurement error is frequently ignored when interpreting epidemiologic study results.

Authors:  Anne M Jurek; George Maldonado; Sander Greenland; Timothy R Church
Journal:  Eur J Epidemiol       Date:  2006-12-21       Impact factor: 8.082

Review 6.  Bias in occupational epidemiology studies.

Authors:  Neil Pearce; Harvey Checkoway; David Kriebel
Journal:  Occup Environ Med       Date:  2006-10-19       Impact factor: 4.402

Review 7.  Bayesian Correction for Exposure Misclassification and Evolution of Evidence in Two Studies of the Association Between Maternal Occupational Exposure to Asthmagens and Risk of Autism Spectrum Disorder.

Authors:  Alison B Singer; M Daniele Fallin; Igor Burstyn
Journal:  Curr Environ Health Rep       Date:  2018-09

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

Authors:  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
Journal:  Stat Med       Date:  2020-04-03       Impact factor: 2.373

Review 9.  STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment.

Authors:  Ruth H Keogh; Pamela A Shaw; Paul Gustafson; Raymond J Carroll; Veronika Deffner; Kevin W Dodd; Helmut Küchenhoff; Janet A Tooze; Michael P Wallace; Victor Kipnis; Laurence S Freedman
Journal:  Stat Med       Date:  2020-04-03       Impact factor: 2.373

10.  Support Vector Machines for Differential Prediction.

Authors:  Finn Kuusisto; Vitor Santos Costa; Houssam Nassif; Elizabeth Burnside; David Page; Jude Shavlik
Journal:  Mach Learn Knowl Discov Databases       Date:  2014
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.