Estimating and communicating spatial certainty when childhood cancers co-cluster

项目摘要/摘要 在疾病集群评估方面有几个最新的进展，如果共同使用，可以避免无数 统计上的错误，包括德克萨斯州夏普枪手谬误。这些进展包括估计以下方面的模型 超越概率(EP)；定义为特定情况下的相对风险 位置大于1。当跨连续空间应用时，EP提供敏感的标识 疾病集群具有不同的集群边界和大小，并具有明确的、空间上不同的确定性。 此外，将模型扩展到多个障碍可以客观地将障碍与普通障碍结合起来 空间模式，从而增加了有效样本量。问题是，儿童癌症是如此罕见 空间参数的先验分布可能会不适当地影响结果。我们最需要的是 当特定CC具有共同的空间风险模式时，结合CC的客观方式。长期目标是 预防因环境暴露引起的疾病。此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式来共享CC子组 常见的空间风险模式。我们的中心假设是CC在一些附近有共同的空间模式 环境危害。这一假设是基于我们的初步发现而提出的。其基本原理是 提出的研究重点是新近发展起来的贝叶斯多元空间建模是联系 我们需要减轻空间不确定性，恢复公众对集群调查的信心。中环 将检验假设，并通过追求以下具体目标来实现这一应用的目标： 1.利用EP的单变量地质统计模型评价单个CC的超额病例数。我们假设， 根据我们目前的研究，EP的地质统计模型将提供更好的敏感性 通过允许灵活的集群形状、大小和统计确定性进行集群检测。2、评估案例过剩 对于多个CC，使用EP的多变量地质统计建模。我们假设，基于我们的初步调查 研究表明，多个CC在一些有毒部位附近共享共同的地理模式和多元建模 CC的使用将提高簇检测的灵敏度。关于预期结果，工作 在目标1中提出的建议将确定一些德克萨斯州超级基金地点附近的个人CC的重大风险模式。目标 2将确定CC在这些地点具有常见的地理风险模式。这一贡献是巨大的 因为我们生活在一个寻求公众对所有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，客观地处理公众的意见。这个 贡献是创新的，因为拟议的研究结合了最近的进展，以解决 疾病聚集性调查中空间不确定性的建模和报告。

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.