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Cox regression with dependent error in covariates
Author(s) -
Huang Yijian,
Wang ChingYun
Publication year - 2018
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12741
Subject(s) - covariate , heteroscedasticity , statistics , econometrics , regression , regression analysis , inference , standard error , observational error , errors in variables models , variance (accounting) , proportional hazards model , nonparametric statistics , mathematics , computer science , artificial intelligence , accounting , business
Summary 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.