标题： 疾病聚类的分离指数 显示英文标题

标题： Segregation Indices for Disease Clustering

摘要： 空间聚类在各个领域都有重要的意义。特别是在流行病学中，疾病聚类引起了公众的广泛关注。本文中，我们提出使用两种基于距离的分离指数来检验在同质或异质人群中，个体位置上的疾病聚类是否显著。我们推导了它们的渐近分布，并通过广泛的蒙特卡洛模拟，从经验大小和功效方面与其他基于距离的疾病聚类检验进行了比较。我们考虑的零假设模式是病例和对照组的随机标记（RL）到给定的位置上。沿着这一思路，我们研究了这些检验在大小上的敏感性，即当RL应用于其下的背景模式（如聚集型或同质型）、聚类水平和聚类数量或类别相对丰度差异时的变化。我们证明了相对丰度差异对检验的经验大小影响最大。我们还提出了各种非RL模式作为RL模式的替代方案，并评估了在这些替代方案下检验的经验功效表现。我们在流行病学中的两个实际例子中展示了这些方法的应用。 显示英文摘要

摘要： Spatial clustering has important implications in various fields. In particular, disease clustering is of major public concern in epidemiology. In this article, we propose the use of two distance-based segregation indices to test the significance of disease clustering among subjects whose locations are from a homogeneous or an inhomogeneous population. We derive their asymptotic distributions and compare them with other distance-based disease clustering tests in terms of empirical size and power by extensive Monte Carlo simulations. The null pattern we consider is the random labeling (RL) of cases and controls to the given locations. Along this line, we investigate the sensitivity of the size of these tests to the underlying background pattern (e.g., clustered or homogenous) on which the RL is applied, the level of clustering and number of clusters, or differences in relative abundances of the classes. We demonstrate that differences in relative abundance has the highest impact on the empirical sizes of the tests. We also propose various non-RL patterns as alternatives to the RL pattern and assess the empirical power performance of the tests under these alternatives. We illustrate the methods on two real-life examples from epidemiology.