Semiparametric bayesian inference for multilevel repeated measurement data.
Semiparametric bayesian inference for multilevel repeated measurement data.
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多级重复测量数据的半参数贝叶斯推理。
DOI：
10.1111/j.1541-0420.2006.00668.x
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发表时间：
2007
期刊：
影响因子：
1.9
通讯作者：
Rosner,GaryL
中科院分区：
文献类型：
--
作者：
Müller,Peter;Quintana,FernandoA;Rosner,GaryL
我们讨论了在多个水平上重复测量的数据的推断。激励的例子是接受多个周期化疗的癌症患者的血细胞计数数据，周期内嵌套了几天。一些推理问题涉及周期内多天的重复测量，而其他问题则涉及跨周期的依赖性。当所需的推理涉及两个重复级别时，在模型中反映数据结构就变得很重要。我们开发了一个半参数贝叶斯建模方法，限制注意两个层次的重复测量。对于顶层纵向抽样模型，我们使用随机效应来引入所需的重复测量相关性。我们对随机效应分布使用非参数先验。通过非参数随机效应模型中隐含的聚类来实现对二级重复依赖性的推断。模型的实际使用要求潜在随机效应的后验分布相当精确。
We discuss inference for data with repeated measurements at multiple levels. The motivating example is data with blood counts from cancer patients undergoing multiple cycles of chemotherapy, with days nested within cycles. Some inference questions relate to repeated measurements over days within cycle, while other questions are concerned with the dependence across cycles. When the desired inference relates to both levels of repetition, it becomes important to reflect the data structure in the model. We develop a semiparametric Bayesian modeling approach, restricting attention to two levels of repeated measurements. For the top-level longitudinal sampling model we use random effects to introduce the desired dependence across repeated measurements. We use a nonparametric prior for the random effects distribution. Inference about dependence across second-level repetition is implemented by the clustering implied in the nonparametric random effects model. Practical use of the model requires that the posterior distribution on the latent random effects be reasonably precise.