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Abstract:
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Observational epidemiological studies often confront the problem of estimating exposure-disease relationships when the exposure is not measured exactly. We investigate exposure measurement error in excess relative risk regression, which is a widely used model in radiation exposure effect research. In the study cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies a generalized version of the classical additive measurement error model, but it may or may not have repeated measurements. In addition, an instrumental variable is available for individuals in a subset of the whole cohort. We develop a non-parametric correction estimator by using data from the subcohort and further propose a joint non-parametric correction estimator using all observed data to adjust for exposure measurement error. An optimal linear combination estimator of the joint non-parametric correction and non-parametric correction is further developed. The estimators proposed are non-parametric, which are consistent without imposing a covariate or error distribution, and are robust to heteroscedastic errors. Finite sample performance is examined via a s
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