The additive hazards model is one of the most commonly used regression models in the analysis of failure time data and many methods have been developed for its inference in various situations. However, no established estimation procedure exists when there are covariates with missing values and the observed responses are interval‐censored; both types of complications arise in various settings including demographic, epidemiological, financial, medical and sociological studies. To address this deficiency, we propose several inverse probability weight‐based and reweighting‐based estimation procedures for the situation where covariate values are missing at random. The resulting estimators of regression model parameters are shown to be consistent and asymptotically normal. The numerical results that we report from a simulation study suggest that the proposed methods work well in practical situations. An application to a childhood cancer survival study is provided. The Canadian Journal of Statistics 48: 499–517; 2020 © 2020 Statistical Society of Canada

中文翻译：

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具有区间删失数据和协变量缺失的加性危害模型估计

加性危害模型是故障时间数据分析中最常用的回归模型之一，并且已经开发出许多在各种情况下进行推断的方法。但是，当协变量存在缺失值且观察到的响应是区间删节时，则不存在已建立的估计程序。两种类型的并发症都发生在各种情况下，包括人口统计学，流行病学，财务，医学和社会学研究。为了解决这一缺陷，针对随机变量缺少协变量的情况，我们提出了几种基于逆概率加权和基于重加权的估计程序。结果表明，回归模型参数的估计是一致且渐近正态的。我们从仿真研究中报告的数值结果表明，所提出的方法在实际情况下效果很好。提供了对儿童癌症生存研究的应用。加拿大统计杂志48：499-517；加拿大 2020©2020加拿大统计学会