title: Expected estimating equation using calibration data for generalized linear models with a mixture of Berkson and classical errors in covariates.
abstract: 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.
authors.affiliation: Division of Public Health, Fred Hutchinson Cancer Research Center, PO Box 19024, Seattle, WA 98109, U.S.A.
authors.forename: Jean de Dieu
authors.initials: JD
authors.lastname: Tapsoba
authors.forename: Shen-Ming
authors.initials: SM
authors.lastname: Lee
authors.forename: Ching-Yun
authors.initials: CY
authors.lastname: Wang
mesh_terms: D016915:AIDS Vaccines / Q000592:standards; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D005260:Female; D006678:HIV / Q000254:growth & development; D015658:HIV Infections / Q000517:prevention & control; D006801:Humans; D016014:Linear Models; D008297:Male; D012044:Regression Analysis
chemical_list: D016915:AIDS Vaccines
keywords: Berkson error; calibration subsample; classical error; expected estimating equation; generalized linear model; instrumental variable