ANALYSIS OF ERROR-CONTAMINATED SURVIVAL DATA UNDER THE PROPORTIONAL ODDS MODEL

Abstract

There has been extensive research interest in analysis of survival data with covariates subject
to measurement error. The focus of the most discussions is on the proportional hazards
(PH) model, although there are some work concerning the accelerated failure time (AFT)
model and the additive hazards (AH) model. Relatively little attention has been directed to
studying the impact of measurement error on other models, such as the proportional odds
(PO) model. The proportional odds model is an important alternative when PH, AFT or
AH models are not appropriate to fit data. In this paper we discuss two inference methods
to accommodate measurement error effects under the PO model, in contrast to the naive
analysis that ignores the covariate measurement error. Numerical studies are conducted to
evaluate the performance of the proposed methods.