NOSI to Support Enhancement of Software Tools for Multilevel Mediation Analysis for Investigating Effects of Environmental and Individual Risk Factors on Respiratory Diseases

考察环境与个体影响的多层次中介分析 呼吸道疾病的危险因素 颗粒物（PM）暴露与呼吸系统健康不良之间的联系很好 确立了习通过项目1的目标3，路易斯安那州立大学超级基金研究中心假设， 来自危险废物场的环境持久性自由基（EPFR）， 焚烧炉/化学火灾和其他来源是PM之间缺少的机械联系 暴露和呼吸系统健康不良。我们将调查该协会的调解使用 分层中介分析，将空气污染物对呼吸系统的不利影响分解为 直接和间接（EPFR介导）效应。 我们开发了一个多层次的中介分析方法，既允许纵向 对居住环境和个别危险因素的评估， 确定暴露于PM和呼吸系统健康不良之间的机械联系。通过 包括我们的方法能够解释的个体行为因素的测量 健康成果方面存在的差距。 我们已经实现了我们的方法作为一个R包（mlma），以提供研究社区 开放访问用于执行多级分层中介分析的软件。但是，在这方面， 使用我们的R软件包确实需要R的知识，这限制了它在研究中的广泛使用。到 为了解决这一限制，我们建议与路易斯安那州立大学的软件工程师合作， API用于我们中介软件的交互式Web实现。我们预计， 大大扩展了对我们方法的访问，因为不需要编程。推荐的APP， 凭借其直观的交互式可视界面，将允许用户轻松读取数据集并构建 使用拖放功能的概念中介模型框架。我们还将提供 图形工具，如有向非循环图，允许用户直观地解释分析 结果此外，我们将把部分计算改造成高效的低层计算机 语言，以提高计算速度，并计划使用云计算资源，使R 和相关的软件包不必安装在单独的计算机上， 进行分析。

Multilevel Mediation Analysis for Investigating Effects of Environmental and Individual Risk Factors on Respiratory Diseases The link between particulate matter (PM) exposure and poor respiratory health is well established. Through Aim 3 of Project 1, the LSU Superfund Research Center postulates that environmentally persistent free radicals (EPFRs) from hazardous waste sites, incinerators/chemical fires, and other sources is the missing mechanistic link between PM exposure and poor respiratory health. We will investigate the mediation of this association using hierarchical mediation analysis to decompose the air pollutant adverse respiratory effects into direct and indirect (EPFR-mediated) effects. We developed a multilevel mediation analysis method that allows for both longitudinal assessments of residential environments and individual risk factors to be jointly utilized in determining the mechanistic link between exposure to PM and poor respiratory health. By including measures of individual behavioral factors our methods are capable of explaining existing disparities in health outcomes. We have implemented our method as an R package (mlma) to provide the research community with open access to software for performing multilevel hierarchical mediation analysis. However, use of our R package does require knowledge of R, which limits its broader use in research. To address this limitation, we propose to collaborate with a software engineer at LSU to create an API for an interactive web implementation of our mediation software. We expect that this would greatly expand access to our methods, as no programming will be required. The proposed app, with its intuitive interactive visual interface, will allow users to easily read in datasets and build a conceptual mediation model framework using drag-and-drop functionality. We will also provide graphical tools such as directed acyclic graphs to allow users to visually interpret the analysis results. In addition, we will transform part of the computing to an efficient low-level computer language to improve computational speed and plan to use cloud computing resources so that R and associated software packages do not have to be installed on individual computers to perform the analysis.