Estimation under Cox proportional hazards model with covariates missing not at random. Issue 18 (17th September 2017)

• Record Type: Journal Article
• Authors: Guo, Lisha; Hu, X. Joan; Liu, Yanyan
• Is Part Of: Communications in statistics. Volume 46:Issue 18(2017)
• Page Start: 8952
• Page End: 8972
• Publication Date: 2017-09-17
• Subjects: Asymptotic normality -- consistency -- semiparametric maximum likelihood estimation -- supplementary information -- variance estimation.; 62N01 -- 62N02 -- 62G20; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2016.1197252
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: This paper considers likelihood-based estimation under the Cox proportional hazards model in the situations where some covariate entries are missing not at random. Assuming the conditional distribution of the missing entries is known, we demonstrate the existence of the semiparametric maximum likelihood estimator of the model parameters, establish the consistency and weak convergence. By simulation, we examine the finite-sample performance of the estimation procedure, and compare the SPMLE with the one resulted from using an estimated conditional distribution of the missing entries. We analyze the data from a tuberculosis (TB) study applying the proposed approach for illustration.