Counting process-based dimension reduction methods for censored outcomes

• 成果类型: Article
• 署名作者: Sun, Qiang; Zhu, Ruoqing; Wang, Tao; Zeng, Donglin
• 署名单位: University of Toronto; University of Illinois System; University of Illinois Urbana-Champaign; Shanghai Jiao Tong University; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asy064
• 发表日期: 2019
• 页码: 181196
• 关键词: sliced inverse regression; models; optimization; variants

摘要

We propose counting process-based dimension reduction methods for right-censored survival data. Semiparametric estimating equations are constructed to estimate the dimension reduction subspace for the failure time model. Our methods address two limitations of existing approaches. First, using the counting process formulation, they do not require estimation of the censoring distribution to compensate for the bias in estimating the dimension reduction subspace. Second, the nonparametric estimation involved adapts to the structural dimension, so our methods circumvent the curse of dimensionality. Asymptotic normality is established for the estimators. We propose a computationally efficient approach that requires only a singular value decomposition to estimate the dimension reduction subspace. Numerical studies suggest that our new approaches exhibit significantly improved performance. The methods are implemented in the package .