Wavelet-based nonparametric modeling of hierarchical functions in colon carcinogenesis
Wavelet-based nonparametric modeling of hierarchical functions in colon carcinogenesis
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DOI：
10.1198/016214503000000422
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发表时间：
2003-09-01
影响因子：
3.7
通讯作者：
Carroll, RJ
中科院分区：
文献类型：
--
作者：
Morris, JS;Vannucci, M;Carroll, RJ
在这篇文章中，我们开发了一种新的方法来分析来自啮齿动物模型的实验数据，以研究饮食脂肪类型对O-6-甲基鸟嘌呤-DNA-甲基转移酶(MGMT)的影响，MGMT是早期结肠癌发生的重要生物标志物。数据由一个空间变量上的观测轮廓组成，该空间变量包含在一个两阶段层次结构中，我们称之为层次功能数据结构。我们提出了一种新的方法，为建模这些数据提供了一个统一的框架，同时产生了均值、个体和亚样本水平分布的估计和后验样本，以及不同层次上的协方差参数。我们的方法是非参数的，因为它不需要预先指定函数的参数形式，并且涉及在小波空间中建模，这对于在MGMT数据中遇到的空间异质函数特别有效。我们的方法是贝叶斯方法；我们模型中唯一有信息的超参数是有效的平滑参数。对这一数据集的分析为MGMT在早期结肠癌发生中的作用以及这可能如何依赖于饮食提供了有趣的新见解。我们的方法是通用的，因此它可以应用到其他遇到分层函数数据的环境中。
In this article we develop new methods for analyzing the data from an experiment using rodent models to investigate the effect of type of dietary fat on O-6-methylguanine-DNA-methyltransferase (MGMT), an important biomarker in early colon carcinogenesis. The data consist of observed profiles over a spatial variable contained within a two-stage hierarchy, a structure that we dub hierarchical functional data. We present a new method providing a unified framework for modeling these data, simultaneously yielding estimates and posterior samples for mean, individual, and subsample-level profiles, as well as covariance parameters at the various hierarchical levels. Our method is nonparametric in that it does not require the prespecification of parametric forms for the functions and involves modeling in the wavelet space, which is especially effective for spatially heterogeneous functions as encountered in the MGMT data. Our approach is Bayesian; the only informative hyperparameters in our model are effectively smoothing parameters. Analysis of this dataset yields interesting new insights into how MGMT operates in early colon carcinogenesis, and how this may depend on diet. Our method is general, so it can be applied to other settings where hierarchical functional data are encountered.