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Cox regression with dependent error in covariates

Huang, Yijian, Wang, Ching‐Yun
Biometrics 2018 v.74 no.1 pp. 118-126
biometry, heteroskedasticity, models, regression analysis, variance
Many survival studies have error‐contaminated covariates due to the lack of a gold standard of measurement. Furthermore, the error distribution can depend on the true covariates but the structure may be difficult to characterize; heteroscedasticity is a common manifestation. We suggest a novel dependent measurement error model with minimal assumptions on the dependence structure, and propose a new functional modeling method for Cox regression when an instrumental variable is available. This proposal accommodates much more general error contamination than existing approaches including nonparametric correction methods of Huang and Wang (2000, Journal of the American Statistical Association 95, 1209–1219; 2006, Statistica Sinica 16, 861–881). The estimated regression coefficients are consistent and asymptotically normal, and a consistent variance estimate is provided for inference. Simulations demonstrate that the procedure performs well even under substantial error contamination. Illustration with a clinical study is provided.