Diagnostic measures for the Cox regression model with missing covariates

• 成果类型: Article
• 署名作者: Zhu, Hongtu; Ibrahim, Joseph G.; Chen, Ming-Hui
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of Connecticut
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asv047
• 发表日期: 2015
• 页码: 907923
• 关键词: proportional hazards regression; local influence; RESIDUALS

摘要

We investigate diagnostic measures for assessing the influence of observations and model misspecification on the Cox regression model when there are missing covariate data. Our diagnostics include case-deletion measures, conditional martingale residuals, and score residuals. The Q-distance is introduced to examine the effects of deleting individual observations on the estimates of finite- and infinite-dimensional parameters. Conditional martingale residuals are used to construct goodness-of-fit statistics for testing misspecification of the model assumptions. A resampling method is developed to approximate the p-values of the goodness-of-fit statistics. We conduct simulation studies to evaluate our methods, and analyse a real dataset to illustrate their use.