Surveillance of Spatial Case Event Data in Cancer Studies

说明(申请人提供)：发展监测空间健康数据的适当分析方法对公共卫生从业人员至关重要，而监测新出现的疾病在空间和时间上的暴发是统计方法中相对不发达的领域。时空监视方面的大量文献大多是为了对完整的数据集进行回顾分析而发展起来的。然而，公共卫生中的数据是随着时间积累的，使用迄今收集的所有数据对模型进行顺序估计是早期发现局部疾病暴发的关键概念。及时治疗和控制措施产生的影响可能是巨大的，特别是在监测癌症发病率地图时，癌症发病率是全球主要死亡原因之一。这个应用程序的目标是开发一种统计方法，用于前瞻性的时空疾病监测，癌症监测是我们的主要重点。这项技术将被用来尽快发现发病率增加的地区，以减少发病率和死亡率。条件预测纵坐标是一种检测异常观测的贝叶斯诊断工具，建议将其扩展到案例事件发生率。我们将在三个具体目标上检验这些假设。在具体目标1中，我们将在案件事件监视设置中调整条件预测纵坐标。在具体目标2中，重点是评估计算方法，并在R包中实施监测工具，R包是许多公共卫生部门提供的一种免费统计编程语言，这将使公共卫生从业人员能够使用它。

DESCRIPTION (provided by applicant): The development of appropriate methods of analysis for surveillance of spatial health data is of critical importance to public health practitioners, yt surveillance for emerging outbreaks of disease in space and time is a relatively undeveloped arena of statistical methodology. Most of the vast literature in space-time surveillance has been developed for retrospective analyses of complete data sets. However, data in public health accumulate over time and sequential estimation of the model using all the data collected so far is a key concept to early detection of localized disease outbreaks. The impact derived from timely treatment and control measures can be dramatic, especially when monitoring maps of cancer disease-incidence, one of the leading causes of death worldwide. The goal of this application is to develop a statistical methodology for prospective spatio- temporal disease surveillance, with cancer surveillance being our primary focus. This technique will be used to detect areas of increased disease incidence as quickly as possible to reduce morbidity and mortality. The conditional predictive ordinate is a Bayesian diagnostic tool that detects unusual observations and it is proposed to extend its use to case event incidence. We will test these hypotheses in three specific aims. In specific Aim 1 we will adapt the conditional predictive ordinate in a case event surveillance setting. In specific Aim 2 the focus is the evaluation of computation approaches, and the implementation of the surveillance tools in an R package, a free statistical programming language available in many public health departments, and this will enable its use by public health practitioners.