In this article, the authors consider a semiparametric additive hazards regression model for right‐censored data that allows some censoring indicators to be missing at random. They develop a class of estimating equations and use an inverse probability weighted approach to estimate the regression parameters. Nonparametric smoothing techniques are employed to estimate the probability of non‐missingness and the conditional probability of an uncensored observation. The asymptotic properties of the resulting estimators are derived. Simulation studies show that the proposed estimators perform well. They motivate and illustrate their methods with data from a brain cancer clinical trial. The Canadian Journal of Statistics 38: 333–351; 2010 © 2010 Statistical Society of Canada

中文翻译：

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随机缺失删失指标的加性风险回归。

在本文中，作者考虑了右删失数据的半参数加性风险回归模型，该模型允许随机丢失一些删失指标。他们开发了一类估计方程，并使用逆概率加权方法来估计回归参数。非参数平滑技术用于估计非缺失概率和未删失观测的条件概率。推导出所得估计量的渐近性质。仿真研究表明，所提出的估计器性能良好。他们用脑癌临床试验的数据来激励和说明他们的方法。加拿大统计杂志38：333-351；2010 © 2010 加拿大统计学会