Evaluation and Development of Cancer Cluster Diagnostics

描述（由申请人提供）： 随着空间模型和时空模型在癌症聚类检测中的应用日益丰富，迫切需要对这些模型进行比较评价，以检验它们在聚类检测中的相对性能。在这个项目中，我们将实现癌症集群建模的三个具体目标。第一个目标将通过对贝叶斯范式内癌症映射中常用的各种空间模型进行比较评估来实现。将考虑一系列计数数据的空间模型，包括随机对象建模、数据相关建模、随机效应建模、混合建模和地质统计建模。在这个比较评估中，我们将在实现复杂性和性能增益之间做出平衡判断。比较将由Hossain和Lawson（2006）开发的一组群集检测诊断进行。还将尝试扩展这些群集检测诊断。第二个目标将通过将集群检测诊断扩展到时空模型来实现。首先，我们建议将Hossain和Lawson（2006）中提出的空间模型的所有诊断扩展到时空模型。为了展示这些时空簇检测诊断的应用，我们将考虑有限数量的时空模型进行比较。最后，第三个目标将通过开发灵活的软件来实现，该软件将包括各种癌症集群诊断，以便被分配癌症集群建模任务的研究人员或公共卫生工作者可以检查首选模型在检测癌症集群方面的能力。该软件将在免费提供的R编程环境中开发。将开发库和图形用户界面以与此包一起使用。通过使用易于访问的软件，我们希望确保我们的方法研究的结果可以随时提供给其他人。

DESCRIPTION (provided by applicant): With the abundance of spatial and spatio-temporal models in cancer cluster detection, there is an urgent need to make a comparative evaluation among these models to check their relative performances in cluster detection. In this project, we will achieve three specific goals in cancer cluster modeling. The first goal will be achieved by making a comparative evaluation of a variety of spatial models that have been commonly used in cancer mapping within a Bayesian paradigm. A range of spatial models for count data will be considered ranging from random object modeling, data-dependent modeling, random effect modeling, mixture modeling and geostatistical modeling. In this comparative evaluation, we will make a balance judgment between implementation complexity and performance gain. The comparison will be made by a set of cluster detection diagnostics developed in Hossain and Lawson (2006). An extension of these cluster detection diagnostics will also be attempted. The second goal will be achieved by extending cluster detection diagnostics to space-time models. Initially, we propose to extend all the diagnostics proposed in Hossain and Lawson (2006) for spatial models to space-time models. To show application of these space-time cluster detection diagnostics, we will consider a limited number of space-time models in comparison. Finally, the third goal will be achieved by developing flexible software, which will include a variety of cancer cluster diagnostics so that researchers or public health workers assigned with the task of cancer cluster modeling can check the ability of a preferred model in detecting cancer cluster. The software will be developed within the freely available R programming environment. Libraries and GUIs will be developed for use with this package. By using readily accessible software we hope to ensure that the results of our methodological investigations can be made readily available to others.