Gangnon R E, Clayton M K
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison 53706, USA.
Biometrics. 2000 Sep;56(3):922-35. doi: 10.1111/j.0006-341x.2000.00922.x.
Many current statistical methods for disease clustering studies are based on a hypothesis testing paradigm. These methods typically do not produce useful estimates of disease rates or cluster risks. In this paper, we develop a Bayesian procedure for drawing inferences about specific models for spatial clustering. The proposed methodology incorporates ideas from image analysis, from Bayesian model averaging, and from model selection. With our approach, we obtain estimates for disease rates and allow for greater flexibility in both the type of clusters and the number of clusters that may be considered. We illustrate the proposed procedure through simulation studies and an analysis of the well-known New York leukemia data.
当前许多用于疾病聚类研究的统计方法都是基于假设检验范式。这些方法通常无法得出疾病发生率或聚类风险的有用估计值。在本文中，我们开发了一种贝叶斯程序，用于对空间聚类的特定模型进行推断。所提出的方法融合了图像分析、贝叶斯模型平均和模型选择的思想。通过我们的方法，我们获得了疾病发生率的估计值，并在聚类类型和可考虑的聚类数量方面都具有更大的灵活性。我们通过模拟研究和对著名的纽约白血病数据的分析来说明所提出的程序。
