Pseudo-partial likelihood estimators for the Cox regression model with missing covariates

• 成果类型: Article
• 署名作者: Luo, Xiaodong; Tsai, Wei Yann; Xu, Qiang
• 署名单位: Icahn School of Medicine at Mount Sinai; Columbia University; US Food & Drug Administration (FDA)
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asp027
• 发表日期: 2009
• 页码: 617633
• 关键词: northern manhattan stroke; hazards regression; ischemic-stroke; case-cohort

摘要

By embedding the missing covariate data into a left-truncated and right-censored survival model, we propose a new class of weighted estimating functions for the Cox regression model with missing covariates. The resulting estimators, called the pseudo-partial likelihood estimators, are shown to be consistent and asymptotically normal. A simulation study demonstrates that, compared with the popular inverse-probability weighted estimators, the new estimators perform better when the observation probability is small and improve efficiency of estimating the missing covariate effects. Application to a practical example is reported.