Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain; InaMAT, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain.
Comput Methods Programs Biomed. 2019 Apr;172:103-116. doi: 10.1016/j.cmpb.2019.02.014. Epub 2019 Feb 25.
Spatial and spatio-temporal analyses of count data are crucial in epidemiology and other fields to unveil spatial and spatio-temporal patterns of incidence and/or mortality risks. However, fitting spatial and spatio-temporal models is not easy for non-expert users. The objective of this paper is to present an interactive and user-friendly web application (named SSTCDapp) for the analysis of spatial and spatio-temporal mortality or incidence data. Although SSTCDapp is simple to use, the underlying statistical theory is well founded and all key issues such as model identifiability, model selection, and several spatial priors and hyperpriors for sensitivity analyses are properly addressed.
The web application is designed to fit an extensive range of fairly complex spatio-temporal models to smooth the very often extremely variable standardized incidence/mortality risks or crude rates. The application is built with the R package shiny and relies on the well founded integrated nested Laplace approximation technique for model fitting and inference.
The use of the web application is shown through the analysis of Spanish spatio-temporal breast cancer data. Different possibilities for the analysis regarding the type of model, model selection criteria, and a range of graphical as well as numerical outputs are provided.
Unlike other software used in disease mapping, SSTCDapp facilitates the fit of complex statistical models to non-experts users without the need of installing any software in their own computers, since all the analyses and computations are made in a powerful remote server. In addition, a desktop version is also available to run the application locally in those cases in which data confidentiality is a serious issue.
在流行病学和其他领域，对计数数据进行空间和时空分析对于揭示发病率和/或死亡率的空间和时空模式至关重要。然而，对于非专业用户来说，拟合空间和时空模型并不容易。本文的目的是介绍一个用于分析空间和时空死亡率或发病率数据的交互式和用户友好的网络应用程序（命名为 SSTCDapp）。尽管 SSTCDapp 使用简单，但基础统计理论是合理的，并且妥善解决了所有关键问题，例如模型可识别性、模型选择以及用于敏感性分析的多个空间先验和超先验。
该网络应用程序旨在拟合广泛的相当复杂的时空模型，以平滑经常非常多变的标准化发病率/死亡率风险或粗率。该应用程序是使用 R 包 shiny 设计的，并依赖于经过充分验证的集成嵌套拉普拉斯逼近技术进行模型拟合和推理。
通过对西班牙时空乳腺癌数据的分析展示了该网络应用程序的使用。提供了针对模型类型、模型选择标准以及一系列图形和数值输出的不同分析可能性。
与疾病映射中使用的其他软件不同，SSTCDapp 为非专业用户提供了拟合复杂统计模型的便利，而无需在他们自己的计算机上安装任何软件，因为所有的分析和计算都是在强大的远程服务器上进行的。此外，还提供了桌面版本，以便在数据保密性是一个严重问题的情况下在本地运行应用程序。
