Efficient Estimation of Semiparametric Transformation Models for the Cumulative Incidence of Competing Risks.
Efficient Estimation of Semiparametric Transformation Models for the Cumulative Incidence of Competing Risks.
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DOI：
10.1111/rssb.12177
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发表时间：
2017-03
期刊：
影响因子：
--
通讯作者：
Lin DY
中科院分区：
文献类型：
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作者：
Mao L;Lin DY
累积发生率是在存在其他风险的情况下，在一定时间段内关注原因导致的失效概率。提出的半参数回归模型已成为制定协变量对累积发生率的影响的首选方法。然而，它的估计需要对删失分布进行建模，并且在统计上是不有效的。在本文中，我们提出了一个广泛的类的半参数转换模型，扩展了精细和灰色模型，我们允许未知的失败原因。我们推导出非参数最大似然估计（NPMLEs）和开发简单而快速的数值算法使用的轮廓似然。我们建立了NPMLEs的一致性，渐近正态性和半参数有效性。此外，我们构建图形和数值程序来评估和选择模型。最后，我们通过广泛的模拟研究和骨髓移植的主要研究的应用程序证明了所提出的方法比现有的方法的优势。
The cumulative incidence is the probability of failure from the cause of interest over a certain time period in the presence of other risks. A semiparametric regression model proposed by has become the method of choice for formulating the effects of covariates on the cumulative incidence. Its estimation, however, requires modeling of the censoring distribution and is not statistically efficient. In this paper, we present a broad class of semiparametric transformation models which extends the Fine and Gray model, and we allow for unknown causes of failure. We derive the nonparametric maximum likelihood estimators (NPMLEs) and develop simple and fast numerical algorithms using the profile likelihood. We establish the consistency, asymptotic normality, and semiparametric efficiency of the NPMLEs. In addition, we construct graphical and numerical procedures to evaluate and select models. Finally, we demonstrate the advantages of the proposed methods over the existing ones through extensive simulation studies and an application to a major study on bone marrow transplantation.