title: A semiparametric copula method for Cox models with covariate measurement error.
abstract: We consider measurement error problem in the Cox model, where the underlying association between the true exposure and its surrogate is unknown, but can be estimated from a validation study. Under this framework, one can accommodate general distributional structures for the error-prone covariates, not restricted to a linear additive measurement error model or Gaussian measurement error. The proposed copula-based approach enables us to fit flexible measurement error models, and to be applicable with an internal or external validation study. Large sample properties are derived and finite sample properties are investigated through extensive simulation studies. The methods are applied to a study of physical activity in relation to breast cancer mortality in the Nurses' Health Study.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA. seheek@umich.edu.
authors.forename: Sehee
authors.initials: S
authors.lastname: Kim
authors.affiliation: Department of Biostatistics, University of Michigan Kidney Epidemiology and Cost Center, University of Michigan, Ann Arbor, MI, USA. yili@umich.edu.
authors.forename: Yi
authors.initials: Y
authors.lastname: Li
authors.affiliation: Departments of Epidemiology, Biostatistics, and Nutrition, Harvard University, Boston, MA, USA. stdls@hsph.harvard.edu.
authors.forename: Donna
authors.initials: D
authors.lastname: Spiegelman
mesh_terms: D001943:Breast Neoplasms / Q000401:mortality / Q000503:physiopathology; D003198:Computer Simulation; D015444:Exercise; D005260:Female; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D016016:Proportional Hazards Models; D012307:Risk Factors; D016019:Survival Analysis
keywords: Bias correction; Copula; Error-prone covariate; Measurement error; Semiparametric; Survival analysis