A Class of Functional Methods for Error-Contaminated Survival Data under Additive Hazards Models with Replicate Measurements

Since Prentice (1982), a large number of inference methods have been developed to handle error-prone data that are modulated with proportional hazards models. In contrast to proportional hazards models, additive hazards models offer a flexible tool to delineate survival processes.

However, there is little research on measurement error effects under additive hazards models. In this paper, we systematically investigate this important problem. New insights of measurement error effects are revealed, as opposed to well-documented results for proportional hazards models.