Chen Xiaolin, Cai Jianwen
School of Statistics, Qufu Normal University, Qufu, 273165, China.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7420, USA.
Lifetime Data Anal. 2018 Apr;24(2):224-249. doi: 10.1007/s10985-017-9398-z. Epub 2017 Aug 1.
Survival data with missing censoring indicators are frequently encountered in biomedical studies. In this paper, we consider statistical inference for this type of data under the additive hazard model. Reweighting methods based on simple and augmented inverse probability are proposed. The asymptotic properties of the proposed estimators are established. Furthermore, we provide a numerical technique for checking adequacy of the fitted model with missing censoring indicators. Our simulation results show that the proposed estimators outperform the simple and augmented inverse probability weighted estimators without reweighting. The proposed methods are illustrated by analyzing a dataset from a breast cancer study.
在生物医学研究中，经常会遇到带有缺失删失指标的生存数据。在本文中，我们考虑在加法风险模型下对这类数据进行统计推断。提出了基于简单和增强逆概率的重加权方法。建立了所提估计量的渐近性质。此外，我们提供了一种数值技术来检验带有缺失删失指标的拟合模型的充分性。我们的模拟结果表明，所提估计量优于未进行重加权的简单和增强逆概率加权估计量。通过分析一项乳腺癌研究的数据集来说明所提方法。
