Analysis of Partially Incomplete Tables of Breast Cancer Characteristics with an Ordinal Variable.
Analysis of Partially Incomplete Tables of Breast Cancer Characteristics with an Ordinal Variable.
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DOI：
10.1080/15598608.2012.719805
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发表时间：
2012-12
影响因子：
0.6
通讯作者：
Munsell MF
中科院分区：
文献类型：
--
作者：
Bekele BN;Nieto-Barajas LE;Munsell MF
我们的目标是对一系列4×2×2×2列联表的联合分布进行建模，其中一些数据部分折叠（即在少至两个维度中聚合）。更具体地说，估计乳腺癌患者的4个临床特征的联合分布。这些特征包括雌激素受体状态（阳性/阴性）、淋巴结受累（阳性/阴性）、HER2-neu表达（阳性/阴性）和疾病分期（I、II、III、IV）。前三个特征的联合分布是根据疾病阶段估计的，我们提出了一个条件概率的动态模型，使它们随着疾病阶段的进展而变化。动态模型基于一系列的狄利克雷分布，这些分布的参数由一个马尔可夫先验结构（称为动态狄利克雷先验）联系起来。该模型利用了跨疾病阶段的信息（称为“借用强度”），并提供了一种估计具有特定肿瘤特征的患者分布的方法。此外，由于一些数据源是聚合的，因此提出了一种数据增强技术来对不同的数据集进行元分析。
Our goal is to model the joint distribution of a series of 4×2×2×2 contingency tables for which some of the data are partially collapsed (i.e., aggregated in as few as two dimensions). More specifically, the joint distribution of 4 clinical characteristics in breast cancer patients is estimated. These characteristics include estrogen receptor status (positive/negative), nodal involvement (positive/negative), HER2-neu expression (positive/negative), and stage of disease (I, II, III, IV). The joint distribution of the first three characteristics is estimated conditional on stage of disease and we propose a dynamic model for the conditional probabilities that let them evolve as the stage of disease progresses. The dynamic model is based on a series of Dirichlet distributions whose parameters are related by a Markov prior structure (called dynamic Dirichlet prior). This model makes use of information across disease stage (known as “borrowing strength”) and provides a way of estimating the distribution of patients with particular tumor characteristics. In addition, since some of the data sources are aggregated, a data augmentation technique is proposed to carry out a meta-analysis of the different datasets.