Efficient estimation of the Cox proportional hazard model incorporating with the auxiliary subgroup information of incidence rate

Abstract

Incidence rate for a disease have been wildly used in the field of medical research because of its clear physical and simple clinical interpretation In this thesis we propose an efficient estimation to incorporate with the auxiliary subgroup information of incidence rate information into the estimation of the Cox proportional hazard model Comparing to the conventional models without incorporation of the available auxiliary information utilizing the external information shows that the proposed method improves efficiency for the estimation of the regression parameters based on the maximum empirical likelihood method In addition simulation studies demonstrate that the proposed method gain in efficiency over the conventional approach

Date of Award	2019
Original language	English
Supervisor	Pei-Fang Su (Supervisor)