Relationship Between Multiple Environmental Exposures and CVD Incidence and Survival: Vulnerability and Susceptibility

项目摘要 家长基金(R01 ES028033)的具体目标是开发新的方法来评估协会 长期接触多种环境因素(空气污染和天气)对几种健康的影响 结果(如心血管疾病风险、死亡率)；并确定易受伤害人群。作为该计划的一部分 在父母拨款的目标1中进行的工作，我们开发了两个科学R包，解决了两个 环境健康中的共同挑战：1)如何灵活地估计在不同环境条件下的暴露-反应曲线 统计方法，允许用户从观测数据分析中评估因果关系1(GPS匹配)； 以及2)如何确定人口中或多或少容易受到以下不利健康影响的亚群 环境暴露2(新)。虽然大多数环境健康研究传统上依赖于现有的 成熟的统计方法，社会上越来越多的人对采用新的前沿技术感兴趣 统计工具，如GPS Match和Denovo。GPS Match和Denovo是有价值的R包和 为研究人员提供了创新的功能，但在软件方面设计得不好 工程最佳实践可供更广泛的科学界使用，而不是针对更大的科学社区进行优化 数据集和云上的高效利用。 通过这项管理建议，我们计划与专业软件建立可持续的协作 工程师重新部署我们的包，并将其传播到更广泛的研究社区。更确切地说， 我们的目标包括：重构和改进GPS匹配和Denovo R包的源代码 为了满足最佳的软件工程实践和社区开源开发，改进 通过优化和并行化R包，并将包移植到Python，为云做好准备。 对GPS匹配和Denovo软件包的重构将使非常大的用户基础 研究人员有效地利用这些工具，向代码库添加功能，并对 算法。该提案将使社区广泛访问这些工具，并将建立工作流 和程序，以便今后更容易地传播其他创新的统计方法。

Project Summary The specific aims of the parent grant (R01 ES028033) are to develop new methods to assess the association between long-term exposure to multiple environmental agents (air pollution and weather) on several health outcomes (e.g. risk of cardiovascular disease, mortality); and identify vulnerable populations. As part of the work conducted in Aim 1 of the parent grant, we developed two scientific R packages that address two common challenges in environmental health: 1) how to estimate flexibly the exposure-response curve under a statistical approach that allows users to assess causality from analyses of observational data1 (GPSmatching); and 2) how to identify subgroups of the population that are more or less vulnerable to adverse health effects of environmental exposure2 (denovo). While most environmental health research traditionally relies on existing well-established statistical approaches, there is increasing interest in the community to adopt new cutting-edge statistical tools, such as GPSmatching and denovo. GPSmatching and denovo are valuable R packages and offer innovative functionalities to researchers, however, they are not well designed in terms of software engineering best practices to be accessible to a broader scientific community and are not optimized for larger datasets and efficient utilization on the cloud. With this administrative proposal, we plan to establish a sustainable collaboration with professional software engineers to redeploy our packages and disseminate them to a wider research community. More specifically, our aims include; refactoring and improving the source code of both the GPSmatching and denovo R packages in order to meet the best software engineering practices and community open source development, improving cloud readiness by optimizing and parallelizing the R packages, and porting the packages to Python. The refactoring of the GPSmatching and denovo software packages will enable a very large user base of researchers to efficiently utilize the tools, add capabilities to the codebase, and offer improvements to the algorithms. This proposal will make these tools widely accessible to the community and will establish workflows and procedures that will allow more easily for the future dissemination of other innovative statistical methods.