Premium
Accounting for error due to misclassification of exposures in case–control studies of gene–environment interaction
Author(s) -
Zhang Li,
Mukherjee Bhramar,
Ghosh Malay,
Gruber Stephen,
Moreno Victor
Publication year - 2007
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.3044
Subject(s) - computer science , statistics , confidence interval , binary number , data set , set (abstract data type) , point estimation , independence (probability theory) , data mining , observational error , artificial intelligence , mathematics , arithmetic , programming language
We consider analysis of data from an unmatched case–control study design with a binary genetic factor and a binary environmental exposure when both genetic and environmental exposures could be potentially misclassified. We devise an estimation strategy that corrects for misclassification errors and also exploits the gene–environment independence assumption. The proposed corrected point estimates and confidence intervals for misclassified data reduce back to standard analytical forms as the misclassification error rates go to zero. We illustrate the methods by simulating unmatched case–control data sets under varying levels of disease–exposure association and with different degrees of misclassification. A real data set on a case–control study of colorectal cancer where a validation subsample is available for assessing genotyping error is used to illustrate our methods. Copyright © 2007 John Wiley & Sons, Ltd.