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Does it always help to adjust for misclassification of a binary outcome in logistic regression?

Authors
Luan-X; Pan-W; Gerberich-SG; Carlin-BP
Source
Stat Med 2005 Jul; 24(14):2221-2234
NIOSHTIC No.
20037631
Abstract
It is well known that in logistic regression, where the outcome is measured with error, a biased estimate of the association between the outcome and a risk factor may result if no proper adjustment is made. Hence, it seems tempting to always adjust for possible misclassification of the outcome. Here we show that it is not always beneficial to do so because, though the adjustment reduces the bias, it also inflates the variance, leading to a possibly larger mean squared error of the estimate. In the context of a data set on agricultural injuries, numerical evidence is provided through simulation studies.
Keywords
Agricultural-industry; Analytical-methods; Analytical-models; Analytical-processes; Injury-prevention; Mathematical-models; Qualitative-analysis; Quality-standards; Risk-analysis; Risk-factors; Statistical-analysis; Statistical-quality-control; Author Keywords: bias; logistic models; mean squared error; measurement error; sensitivity and specificity; simulation
Contact
Bradley P. Carlin, Division of Biostatistics, University of Minnesota, School of Public Health, Box 303 Mayo Memorial Building, 420 Delaware St., Minneapolis, MN 55455-0378, U.S.A
CODEN
SMEDDA
Publication Date
20050730
Document Type
Journal Article
Email Address
brad@biostat.umn.edu
Funding Type
Grant
Fiscal Year
2005
NTIS Accession No.
NTIS Price
Identifying No.
Grant-Number-T42-OH-008434
Issue of Publication
14
ISSN
0277-6715
Source Name
Statistics in Medicine
State
MN; NE
Performing Organization
University of Minnesota Twin Cities
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