EXACT DISEASE CLUSTER STATISTICS FOR STRUCTURED SETTINGS

卫生专业人员必须经常分析疾病聚集性指控 由忧心忡忡的公民提出，他们感觉到了小结构中的模式 环境。例子包括家庭、家庭、兄弟姐妹关系、病房、 牢房、教室、工作类型、年龄阶层和地点 大楼。对于健康专业人员来说，了解哪些测试是至关重要的 在这些小环境问题上表现最好。 一些大规模的方法可能会被修改以处理小环境 有问题。然而，由于样本量很小，准确的方法可能是 好多了。我们扩展了以前的矩阵占用方法，以产生新的、 用于模式分析的快速、准确的组合表达式 患病的个体。我们建议调查这两个因素的力量 以及结构化小型环境中的其他先前发布的方法。 据我们所知，还没有进行过这样的系统调查。 我们预计，许多测试方法将在有限的范围内达到最佳 参数空间，但在其他地方并不理想。我们的长期目标是 要使用此电源模拟的结果来选择 整合到Exclude中，这是一个用户友好的PC程序，用于分析小型 结构化环境数据，并帮助专业人员选择 正确的测试方法。

Health professionals must often analyze disease cluster allegations made by concerned citizens who sense patterns within small structured environments. Examples include households, families, sibships, wards, cellblocks, classrooms, job types, age classes, and locations within a building. It is crucial for the health professional to know what tests are best in these small-environment problems. A few large-scale methods may be modified to handle small-environment problems. However, because sample sizes are small, exact methods may be better. We have extended previous matrix-occupancy methods to yield new, rapid, and exact combinatorial expressions for analysis of patterns of diseased individuals. We propose to investigate the power of both these and other previously-published methods in structured small environments. To our knowledge, no such systematic investigation has been carried out. We expect that many test methods will be optimal in a restricted range of the parameter space, but not ideal elsewhere. Our long-range goal is to use the results of this power simulation to choose methods for incorporation into EXCLUDE, a user-friendly PC program to analyze small structured environment data, and to help the professional choose the right test method.