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Estimating and communicating spatial certainty when childhood cancers co-cluster

估计和传达儿童癌症共簇时的空间确定性

基本信息

批准号：
9535249
负责人：
JAMES A THOMPSON
金额：
$ 7.43万
依托单位：
TEXAS A&M UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2021-08-31
项目状态：
已结题

项目摘要

PROJECT SUMMARY/ABSTRACT There are several recent advances, in disease cluster evaluation that if used collectively could avoid a myriad of statistical faults, including the Texas Sharp Shooter Fallacy. These advances include models that estimate the exceedance probability (EP); defined as the Bayesian probability that the relative risk at a specific location is greater than 1. When applied across continuous space, the EP provides a sensitive identification of disease clusters with varying cluster boundaries and sizes and with explicit, spatially-varying certainty. Furthermore, extending the model to multiple disorders can objectively combine disorders with common spatial patterns thereby enhancing the effective sample size. The problem is that childhood cancer is so rare that the prior distributions for the spatial parameters may unduly influence the results. What we need most is an objective way to combine CC when specific CC have common spatial risk patterns. The long-term goal is to prevent diseases caused by environmental exposures. The overall objective of this application, which is the next step in our long-term goal, is to find the most objective way to pool CC subgroups when they share common spatial risk patterns. Our central hypothesis is that CC have common spatial patterns near some environmental hazards. The hypothesis is formulated based on our preliminary findings. The rationale that underlies the proposed research is that recently developed Bayesian multivariate spatial modeling is the link that we need to mitigate spatial uncertainty and restore public faith in cluster investigations. The central hypothesis will be tested and the objective of this application attained by pursuing the following specific aims: 1. Evaluate case-excess for single CC using univariate geostatistical modeling of EP. We postulate, based on our current studies, that geostatistical modeling of the EP will provide an improved sensitivity for cluster detection by allowing flexible cluster shapes, sizes and statistical certainty. 2, Evaluate case-excess for multiple CC using multivariate geostatistical modeling of EP. We postulate, based on our preliminary studies, that multiple CC share common geographic patterns near some toxic sites and multivariate modeling of the CC will enhance the sensitivity of cluster detection. With respect to expected outcomes, the work proposed in aim 1 will identify significant risk patterns of individual CC near some Texas Superfund Sites. Aim 2 will identify CC with common geographic risk patterns at these locations. This contribution is significant because we live in an era in which the public's vigilance is sought for all environmental risks and the public's input must be encouraged and validated and, most importantly, addressed, objectively. The contribution is innovative because the proposed research combines recent advances to resolve issues in the modeling and reporting of spatial uncertainty in disease cluster investigation.

项目概要/摘要在疾病集群评估方面有一些最新进展，如果共同使用，可以避免无数的疾病统计错误，包括德克萨斯神枪手谬误。这些进步包括估计模型超出概率（EP）；定义为特定情况下相对风险的贝叶斯概率位置大于 1。当应用于连续空间时，EP 提供敏感的识别具有不同簇边界和大小以及明确的、空间变化的确定性的疾病簇。此外，将模型扩展到多种疾病可以客观地将疾病与常见疾病结合起来。空间模式，从而增加有效样本量。问题是儿童癌症非常罕见空间参数的先验分布可能会对结果产生过度影响。我们最需要的是当特定 CC 具有共同的空间风险模式时，一种客观的组合 CC 的方法。长期目标是预防因环境暴露引起的疾病。该应用程序的总体目标是我们长期目标的下一步是找到最客观的方法来汇集 CC 子组，当它们共享时常见的空间风险模式。我们的中心假设是，CC 在某些区域附近具有共同的空间模式环境危害。该假设是根据我们的初步发现制定的。理由是所提出的研究的基础是最近开发的贝叶斯多元空间模型是链接我们需要减轻空间不确定性并恢复公众对集群调查的信心。中央将测试假设，并通过追求以下具体目标来实现本应用程序的目标： 1. 使用 EP 的单变量地质统计模型评估单个 CC 的病例过剩情况。我们假设，根据我们目前的研究，EP 的地质统计模型将为通过允许灵活的簇形状、大小和统计确定性来进行簇检测。 2、评估case-excess 使用 EP 多元地质统计模型进行多个 CC。我们假设，根据我们的初步研究表明，多个 CC 在一些有毒地点附近具有共同的地理模式和多变量模型 CC的使用将提高簇检测的灵敏度。就预期成果而言，工作目标 1 中提出的目标将确定一些德克萨斯州超级基金站点附近单个 CC 的显着风险模式。目的 2 将识别这些地点具有常见地理风险模式的 CC。这个贡献意义重大因为我们生活在一个公众对一切环境风险保持警惕的时代，必须鼓励和验证公众的意见，最重要的是，必须客观地处理公众的意见。这贡献具有创新性，因为拟议的研究结合了解决问题的最新进展疾病集群调查中空间不确定性的建模和报告。