Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/18414

Maximum likelihood inference for the Cox regression model with applications to missing covariates

Authors Chen, Ming-Hui
Ibrahim, Joseph G.
Shao, Qi-Man View this author's profile
Issue Date 2009
Source Journal of multivariate analysis , v. 100, (9), 2009, OCT, p. 2018-2030
Summary In this paper, we carry out an in-depth theoretical investigation for existence of maximum likelihood estimates for the Cox model [D.R. Cox, Regression models and life tables (with discussion), journal of the Royal Statistical Society, Series B 34 (1972) 187-220; D.R. Cox, Partial likelihood, Biometrika 62 (1975) 269-276] both in the full data setting as well as in the presence of missing covariate data. The main motivation for this work arises from missing data problems, where models can easily become difficult to estimate with certain missing data configurations or large missing data fractions. We establish necessary and sufficient conditions for existence of the maximum partial likelihood estimate (MPLE) for completely observed data (i.e., no missing data) settings as well as sufficient conditions for existence of the maximum likelihood estimate (MLE) for survival data with missing covariates via a profile likelihood method. Several theorems are given to establish these conditions. A real dataset from a cancer clinical trial is presented to further illustrate the proposed methodology. (C) 2009 Elsevier Inc. All rights reserved.
Subjects
ISSN 0047-259X
Language English
Format Article
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