In this talk, we propose a consistent estimation procedure for Cox regression under the additive measurement error model. Distinct from existing methods, the proposed estimation for regression coefficients does not require any additional assumptions. By establishing the normalized partial-score function as a functional of empirical processes, we construct an estimating function with the same limit using replicated mismeasured covariates. We also suggest estimators for the baseline cumulative hazard function.
Numerical studies demonstrate that the procedure performs well under practical sample sizes.
Application to an AIDS clinical trial is provided. Joint work with C. Y. Wang, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center.