Frailty Modeling via the Empirical Bayes Hastings Sampler.
Frailty Modeling via the Empirical Bayes Hastings Sampler.
复制标题
通过经验贝叶斯黑斯廷斯采样器进行脆弱性建模。
DOI：
10.1016/j.csda.2011.09.004
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发表时间：
2012
影响因子：
1.8
通讯作者：
Demirel,Shaban
中科院分区：
文献类型：
--
作者：
Levine,RichardA;Fan,Juanjuan;Strickland,PamelaOhman;Demirel,Shaban
眼部疾病的研究和疾病发作时间的分析由于预期来自单个患者的两只眼睛之间的相关性而变得复杂。我们通过非参数贝叶斯脆弱模型克服了这些统计建模挑战。虽然该模型表明其本身是用于这种复杂数据结构的自然模型，但是考虑到针对脆弱性分布和基线风险函数假设的非参数形式，模型拟合例程变得极其复杂和计算密集。我们认为，经验贝叶斯方法，以减轻这些困难，通过一个例行的迭代之间的频率，数据驱动的估计的累积基线风险和马尔可夫链蒙特卡罗估计的脆弱性和回归系数。我们在理论上和通过模拟表明，这种方法产生一致的估计参数的兴趣。然后，我们将该方法应用于短波自动视野检查（SWAP）数据集，以研究昏迷性视野缺损的危险因素。
Studies of ocular disease and analyses of time to disease onset are complicated by the correlation expected between the two eyes from a single patient. We overcome these statistical modeling challenges through a nonparametric Bayesian frailty model. While this model suggests itself as a natural one for such complex data structures, model fitting routines become overwhelmingly complicated and computationally intensive given the nonparametric form assumed for the frailty distribution and baseline hazard function. We consider empirical Bayesian methods to alleviate these difficulties through a routine that iterates between frequentist, data-driven estimation of the cumulative baseline hazard and Markov chain Monte Carlo estimation of the frailty and regression coefficients. We show both in theory and through simulation that this approach yields consistent estimators of the parameters of interest. We then apply the method to the short-wave automated perimetry (SWAP) data set to study risk factors of glaucomatous visual field deficits.