Impact of additive covariate error on linear model. Issue 22 (17th November 2019)

• Record Type: Journal Article
• Authors: Nakashima, Eiji
• Is Part Of: Communications in statistics. Volume 48:Issue 22(2019)
• Page Start: 5517
• Page End: 5529
• Publication Date: 2019-11-17
• Subjects: Additive measurement error -- GLMs -- identity-link -- LNT model -- regression calibration; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2018.1515361
• Languages: English
• ISSNs: 0361-0926

Abstract

Abstract: We consider effect of additive covariate error on linear model in observational (radiation epidemiology) study for exposure risk. Additive dose error affects dose-response shape under general linear regression settings covering identity-link GLM type models and linear excess-relative-risk grouped-Poisson models. Under independent error, dose distribution that log of dose density is up to quadratic polynomial on an interval (the log-quadratic density condition), normal, exponential, and uniform distributions, is the condition for linear regression calibration. Violation of the condition can result low-dose-high-sensitivity model from linear no-threshold (LNT) model by the dose error. Power density is also considered. A published example is given.