Partially linear hazard regression for multivariate survival data

• 成果类型: Article
• 署名作者: Cai, Jianwen; Fan, Jianqing; Jiang, Jiancheng; Zhou, Haibo
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; Princeton University; University of North Carolina; University of North Carolina Charlotte
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214506000001374
• 发表日期: 2007
• 页码: 538-551
• 关键词: FAILURE TIME DATA; likelihood estimation; MODEL

摘要

This article studies estimation of partially linear hazard regression models for multivariate survival data. A profile pseudo-partial likelihood estimation method is proposed under the marginal hazard model framework. The estimation on the parameters for the linear part is accomplished by maximization of a pseudo-partial likelihood profiled over the nonparametric part. This enables us to obtain root n-consistent estimators of the parametric component. Asymptotic normality is obtained for the estimates of both the linear and nonlinear parts. The new technical challenge is that the nonparametric component is indirectly estimated through its integrated derivative function from a local polynomial fit. An algorithm of fast implementation of our proposed method is presented. Consistent standard error estimates using sandwich-type ideas are also developed, which facilitates inferences for the model. It is shown that the nonparametric component can be estimated as well as if the parametric components were known and the failure times within each subject were independent. Simulations are conducted to demonstrate the performance of the proposed method. A real dataset is analyzed to illustrate the proposed methodology.