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Does it always help to adjust for misclassification of a binary outcome in logistic regression?
Author(s) -
Luan Xianqun,
Pan Wei,
Gerberich Susan G.,
Carlin Bradley P.
Publication year - 2005
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2094
Subject(s) - logistic regression , outcome (game theory) , context (archaeology) , statistics , variance (accounting) , regression , econometrics , computer science , binary data , regression analysis , binary number , mathematics , economics , paleontology , accounting , mathematical economics , biology , arithmetic
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. Copyright © 2005 John Wiley & Sons, Ltd.