Title: Rank estimation for the accelerated failure time model with partially interval-censored data Show Chinese title

Title: 具有部分区间删失数据的加速失效时间模型的秩估计

Abstract: This paper presents a unified rank-based inferential procedure for fitting the accelerated failure time model to partially interval-censored data. A Gehan-type monotone estimating function is constructed based on the idea of the familiar weighted log-rank test, and an extension to a general class of rank-based estimating functions is suggested. The proposed estimators can be obtained via linear programming and are shown to be consistent and asymptotically normal via standard empirical process theory. Unlike common maximum likelihood-based estimators for partially interval-censored regression models, our approach can directly provide a regression coefficient estimator without involving a complex nonparametric estimation of the underlying residual distribution function. An efficient variance estimation procedure for the regression coefficient estimator is considered. Moreover, we extend the proposed rank-based procedure to the linear regression analysis of multivariate clustered partially interval-censored data. The finite-sample operating characteristics of our approach are examined via simulation studies. Data example from a colorectal cancer study illustrates the practical usefulness of the method. Show Chinese abstract

Abstract: 本文提出了一种统一的基于秩次的推断程序，用于拟合加速失效时间模型到部分区间删失数据。 基于熟悉的加权对数秩检验的思想构建了一个 Gehan 型单调估计函数，并建议将其扩展到一类广义的基于秩次的估计函数。 所提出的估计量可以通过线性规划获得，并通过标准的经验过程理论证明其具有一致性和渐近正态性。 与常见的基于最大似然估计的部分区间删失回归模型估计器不同，我们的方法可以直接提供回归系数估计量，而无需涉及潜在残差分布函数的复杂非参数估计。 考虑了回归系数估计量的有效方差估计程序。 此外，我们将所提出的基于秩次的方法扩展到多元聚类部分区间删失数据的线性回归分析。 通过模拟研究检查了我们方法的有限样本操作特性。 来自结直肠癌研究的数据实例展示了该方法的实际实用性。