Geostatistical software for spatial and multi-dimensional joinpoint regression analysis of time series of health outcomes

 描述(由申请人提供)：分析癌症发病率和死亡率的时间趋势可以提供关于疾病负担的更全面的图景，并对各种干预措施的影响产生新的见解。NCI监测研究计划开发的连接点回归越来越多地用于确定健康结果时间序列的变化时间和程度，并通过预测未来新癌症病例或死亡人数来预测未来的癌症负担。然而，对空间框架外的时间趋势的分析并不令人满意，因为人们很早就认识到，美国各县和州之间的癌症发病率存在显著差异。因此，至关重要的是在地理信息系统(GIS)内实施连接点回归，并开发界面，提供用户友好的工具，用于对健康结果的大型时间序列进行预处理、建模、可视化和汇总。这个SBIR项目正在开发第一个商业软件，为健康结果时间序列的地质统计建模和连接点回归分析提供工具。该研究产品将是由Esri的合作伙伴BioMedware开发的桌面时空可视化核心中的一个独立模块。该软件包将提供一套全面的软件，用于：1)不同空间尺度(如邮政编码、县)的健康结果时间序列的计算和地统计噪声过滤(克里格法)；2)回归模型的参数(如结合点年、平均年变化百分比)如何在空间和空间尺度上变化的可视化；以及3)通过多维尺度和聚类分析分析时间序列之间的相似性及其聚合。这些工具将适用于分析健康科学以外的数据，如犯罪测绘、鱼类种群评估或气候变化，从而大大拓宽最终产品的商业市场。该项目将实现三个目标：进行基于模拟的研究，以评估以下方法的益处：1)将连接点回归应用于平滑的时间序列(基于克里金法 和贝叶斯过滤器)，用于从以小地理单元记录的不稳定比率中识别时间趋势，2)多维尺度，以可视化时间序列集合之间的差异，以及3)聚类分析，以分组具有相似时间趋势的地理单元。开发和测试一个原型模块，该模块将基于BioMedware的时空可视化和分析技术，指导用户创建、连接点回归建模、可视化和健康结果时间序列的多维分析。进行可用性研究，并确定在第二阶段要考虑的其他方法和工具。这些技术、科学和商业创新将彻底改变我们检测癌症发病率和死亡率跨空间和跨时间变化的能力，带来重要的信息和知识，这些信息和知识将大大有助于癌症流行病学、控制和监测，并有助于缩小这些差距。

 DESCRIPTION (provided by applicant): Analyzing temporal trends in cancer incidence and mortality rates can provide a more comprehensive picture of the burden of the disease and generate new insights about the impact of various interventions. Join point regression developed by NCI Surveillance Research Program is increasingly used to identify the timing and extent of changes in time series of health outcomes and to project future cancer burden through the prediction of the future number of new cancer cases or deaths. The analysis of temporal trends outside a spatial framework is however unsatisfactory, since it has long been recognized that there is significant variation among U.S. counties and states with regard to the incidence of cancer. It is thus critical to implement join point regression within Geographical Information Systems (GIS), and develop interfaces offering user-friendly tools for pre-processing, modeling, visualizing and summarizing large ensembles of time series of health outcomes. This SBIR project is developing the first commercial software to offer tools for the geostatistical modeling and join point regression analysis of time series of health outcomes. The research product will be a stand-alone module into the desktop space-time visualization core developed by BioMedware, an Esri partner. This software package will provide a comprehensive suite for: 1) the computation and geostatistical noise-filtering (kriging) of time series of health outcomes at various spatial scales (e.g. ZIP codes, counties), 2) the visualization of how the parameters of the regression model (e.g. join point years, Average Annual Percent Change) change in space and across spatial scales, and 3) the analysis of similarities among time series and their aggregation through multi-dimensional scaling and clustering analysis. These tools will be suited for the analysis of data outside health sciences, such as in crime mapping, fish stock assessment or climate change, broadening significantly the commercial market for the end product. This project will accomplish three aims:  Conduct simulation-based studies to assess the benefits of: 1) the application of join point regression to smoothed time series (kriging-based and Bayesian filters) for identifying temporal trends from unstable rates recorded in small geographical units, 2) multi-dimensional scaling to visualize differences among ensemble of time series, and 3) clustering analysis to group geographical units with similar temporal trend. Develop and test a prototype module that will guide users through the creation, join point regression modeling, visualization and multi-dimensional analysis of time series of health outcomes, based on BioMedware's space-time visualization and analysis technology.  Conduct a usability study and identify additional methods and tools to consider in Phase II. These technologic, scientific and commercial innovations will revolutionize our ability to detect changes in cancer incidence and mortality across space and through time, bringing important information and knowledge that will benefit substantially cancer epidemiology, control and surveillance and help reducing these disparities.