Reweighting Estimators for Cox Regression With Missing Covariates

• 成果类型: Article
• 署名作者: Xu, Qiang; Paik, Myunghee Cho; Luo, Xiaodong; Tsai, Wei-Yann
• 署名单位: US Food & Drug Administration (FDA); Columbia University; Icahn School of Medicine at Mount Sinai
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.tm07172
• 发表日期: 2009
• 页码: 1155-1167
• 关键词: proportional hazards regression; MODEL; likelihood; EQUATIONS

摘要

Missingness in covariates is a common problem in survival data. In this article we propose a reweighting method for estimating the regression parameters in the Cox model with missing covariates. We also consider the augmented reweighting method by subtracting the projection term onto the nuisance tangent space. The proposed method provides consistent and asymptotically normally distributed estimators when the missing-data mechanism depends on the outcome variables, its well as on the observed covariates with either monotone or arbitrary nonmonotone missingness patterns. Simulation results indicate that in most Situations, the proposed reweighting estimators are more efficient than the inverse probability weighting estimators for the regression coefficients of the missing covariates and are as efficient its or more efficient than the inverse probability weighting estimators for the regression coefficients of the completely observed covariates. The simple reweighting estimators can be easily implemented in standard statistical packages.