SEMIPARAMETRIC TRANSFORMATION MODELS WITH RANDOM EFFECTS FOR CLUSTERED FAILURE TIME DATA.
SEMIPARAMETRIC TRANSFORMATION MODELS WITH RANDOM EFFECTS FOR CLUSTERED FAILURE TIME DATA.
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DOI：
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发表时间：
2008
影响因子：
1.4
通讯作者：
D. Zeng;Danyu Lin;Xihong Lin
中科院分区：
文献类型：
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作者：
D. Zeng;Danyu Lin;Xihong Lin
我们提出了一个一般类的半参数转换模型与随机效应，制定可能随时间变化的协变量对集群或相关的故障时间的影响。这个类别涵盖了所有常用的转换模型，包括比例风险和比例优势模型，它适应各种随机效应分布，特别是高斯分布。证明了模型参数的非参数极大似然估计是相合的、渐近正态的和渐近有效的。我们开发了相应的基于似然的推理程序。仿真研究表明，所提出的方法在实际情况下表现良好。提供了一个众所周知的糖尿病视网膜病变研究的例子。
We propose a general class of semiparametric transformation models with random effects to formulate the effects of possibly time-dependent covariates on clustered or correlated failure times. This class encompasses all commonly used transformation models, including proportional hazards and proportional odds models, and it accommodates a variety of random-effects distributions, particularly Gaussian distributions. We show that the nonparametric maximum likelihood estimators of the model parameters are consistent, asymptotically normal and asymptotically efficient. We develop the corresponding likelihood-based inference procedures. Simulation studies demonstrate that the proposed methods perform well in practical situations. An illustration with a well-known diabetic retinopathy study is provided.