Evaluating the exposure and disease relationship with adjustment for different types of exposure misclassification: a regression approach.
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.
Kosinski
A S
AS
Department of Biostatistics, The Rollins School of Public Health, 1518 Clifton Road, N.E., Emory University, Atlanta, Georgia 30322, USA. akosins@sph.emory.edu
Flanders
W D
WD
D000465:Algorithms; D015906:Angioplasty, Balloon, Coronary / Q000706:statistics & numerical data; D015982:Bias; D001026:Coronary Artery Bypass / Q000706:statistics & numerical data; D003327:Coronary Disease / Q000628:therapy; D005080:Exercise Test / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D016032:Randomized Controlled Trials as Topic; D012044:Regression Analysis; D015899:Tomography, Emission-Computed, Single-Photon / Q000706:statistics & numerical data