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Evaluating the exposure and disease relationship with adjustment for different types of exposure misclassification: a regression approach
Author(s) -
Kosinski Andrzej S.,
Flanders W. Dana
Publication year - 1999
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/(sici)1097-0258(19991030)18:20<2795::aid-sim192>3.0.co;2-s
Subject(s) - statistics , logistic regression , gold standard (test) , regression , computer science , regression analysis , econometrics , linear regression , standard error , mathematics
Misclassification of exposure can lead to biased results in the epidemiologic research. Available methods accounting for misclassification often require the use of a gold standard or assume non‐differential misclassification of exposure. We present a regression approach which can detect and account for different types of misclassification when estimating the exposure and disease relationship. This approach uses two imperfect measures of a dichotomous exposure and does not require a gold standard. Standard statistical packages with a logistic regression module can be used for estimation of parameters through the EM algorithm process. Two examples are used to illustrate the methodology. Copyright © 1999 John Wiley & Sons, Ltd.