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Estimation of the additive hazards model with current status data in the presence of informative censoring

机译：估计信息审查中当前状态数据的添加剂危险模型

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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 under various situations. This paper discusses the situation where one faces current status data and also there exists informative censoring or when the failure time of interest and the observation process are correlated. Several authors have discussed the problem and in particular, Zhang et al. (2005) and Zhao et al. (2015) proposed an estimating equation-based approach and a copula model-based method, respectively. However, the former may not be efficient and the latter needs some restrictive assumptions. To address these, we propose a sieve maximum likelihood estimation approach that can be more efficient and also does not require the assumption above. For the implementation of the method, an EM algorithm is developed and the asymptotic properties of the resulting estimators are established. The numerical results suggest that the proposed method works well in practical situations and an application is provided.
机译：添加剂危险模型是故障时间数据分析中最常用的回归模型之一，并且在各种情况下已经推断出了许多方法。本文讨论了一个面对当前状态数据的情况，并且还存在信息丰富的审查或者当感兴趣的失败时间和观察过程相关。几位作者讨论了这个问题，特别是张等人。 （2005）和Zhao等人。 （2015）提出了一种估计基于方程的方法和基于谱模型的方法。然而，前者可能不会有效，后者需要一些限制性假设。为了解决这些问题，我们提出了一种筛位最大似然估计方法，可以更有效，并且也不需要上面的假设。为了实现该方法，开发了EM算法，建立了所得估计器的渐近特性。数值结果表明，所提出的方法在实际情况下运行良好，提供了应用。

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