The use of Gaussian quadrature for estimation in frailty proportional hazards models
The use of Gaussian quadrature for estimation in frailty proportional hazards models
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DOI：
10.1002/sim.3077
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发表时间：
2008-06-30
影响因子：
2
通讯作者：
Huang, Xuelin
中科院分区：
文献类型：
--
作者：
Liu, Lei;Huang, Xuelin
本文提出了一种新的Gaussian求积估计方法。我们通过分段常数近似未指定的基线风险，从而得到一个参数模型，该模型可以通过SAS Proc NLMIXED等标准软件中的高斯正交工具方便地拟合。我们首先将我们的方法应用于简单的脆弱模型相关的生存数据（如复发或集群故障时间），然后联合脆弱模型的相关故障时间与信息辍学或依赖终端事件，如死亡。仿真研究表明，我们的方法相比，深受欢迎的惩罚偏似然方法和蒙特卡罗EM（MCEM）方法，正常和伽玛脆弱性模型。我们将我们的方法应用于三个真实的数据实例：（1）糖尿病视网膜病变研究中双眼失明的时间，（2）HIV感染患者死亡情况下复发性机会性疾病的联合分析，以及（3）软组织肉瘤研究中局部、远处肿瘤复发和患者生存的联合建模。所提出的方法大大简化了（联合）脆弱性模型的实施，使他们更容易获得一般的统计从业人员。版权所有（C）2007约翰威利父子有限公司
In this paper, we propose a novel Gaussian quadrature estimation method in various frailty proportional hazards models. We approximate the unspecified baseline hazard by a piecewise constant one, resulting in a parametric model that can be fitted conveniently by Gaussian quadrature tools in standard software such as SAS Proc NLMIXED. We first apply our method to simple frailty models for correlated survival data (e.g. recurrent or clustered failure times), then to joint frailty models for correlated failure times with informative dropout or a dependent terminal event such as death. Simulation studies show that our method compares favorably with the well-received penalized partial likelihood method and the Monte Carlo EM (MCEM) method, for both normal and Gamma frailty models. We apply our method to three real data examples: (1) the time to blindness of both eyes in a diabetic retinopathy study, (2) the joint analysis of recurrent opportunistic diseases in the presence of death for HIV-infected patients, and (3) the joint modeling of local, distant tumor recurrences and patients survival in a soft tissue sarcoma study. The proposed method greatly simplifies the implementation of the (joint) frailty models and makes them much more accessible to general statistical practitioners. Copyright (C) 2007 John Wiley & Sons, Ltd.