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Expected estimating equation using calibration data for generalized linear models with a mixture of Berkson and classical errors in covariates
Author(s) -
Tapsoba Jean de Dieu,
Lee ShenMing,
Wang ChingYun
Publication year - 2013
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.5966
Subject(s) - covariate , statistics , calibration , observational error , generalized linear model , inference , mathematics , errors in variables models , linear regression , statistical inference , econometrics , regression , regression analysis , computer science , artificial intelligence
Data collected in many epidemiological or clinical research studies are often contaminated with measurement errors that may be of classical or Berkson error type. The measurement error may also be a combination of both classical and Berkson errors and failure to account for both errors could lead to unreliable inference in many situations. We consider regression analysis in generalized linear models when some covariates are prone to a mixture of Berkson and classical errors, and calibration data are available only for some subjects in a subsample. We propose an expected estimating equation approach to accommodate both errors in generalized linear regression analyses. The proposed method can consistently estimate the classical and Berkson error variances based on the available data, without knowing the mixture percentage. We investigated its finite‐sample performance numerically. Our method is illustrated by an application to real data from an HIV vaccine study. Copyright © 2013 John Wiley & Sons, Ltd.