Modeling spatial survival data using semiparametric frailty models
Modeling spatial survival data using semiparametric frailty models
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DOI：
10.1111/j.0006-341x.2002.00287.x
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发表时间：
2002-06-01
期刊：
影响因子：
1.9
通讯作者：
Ryan, L
中科院分区：
文献类型：
--
作者：
Li, Y;Ryan, L
我们提出了一类新的半参数脆弱性模型的空间相关生存数据。具体而言，我们扩展了普通脆弱性模型，允许容纳空间相关性的随机效应乘法地进入基线危害函数。证明了模型的可辨识性，并给出了充分的正则性条件。我们提出了基于边际秩似然的绘图推理。在这种半参数方法中，不需要假设基线危险的参数形式。采用蒙特卡罗模拟和拉普拉斯方法处理似然函数中的难解积分。在模拟中探索了不同的空间协方差结构，并将提出的方法应用于东波士顿哮喘研究，以检测导致儿童哮喘的预后因素。
We propose a new class of semiparametric frailty models for spatially correlated survival data. Specifically, we extend the ordinary frailty models by allowing random effects accommodating spatial correlations to enter into the baseline hazard function multiplicatively. We prove identifiability of the models and give sufficient regularity conditions. We propose drawing inference based on a marginal rank likelihood. No parametric forms of the baseline hazard need to be assumed in this semiparametric approach. Monte Carlo simulations and the Laplace approach are used to tackle the intractable integral in the likelihood function. Different spatial covariance structures are explored in simulations and the proposed methods are applied to the East Boston Asthma Study to detect prognostic factors leading to childhood asthma.