Estimating and communicating spatial certainty when childhood cancers co-cluster

项目概要/摘要 最近在疾病集群评估方面取得了一些进展，如果共同使用，可以避免无数的 统计错误，包括德克萨斯州夏普射手谬误。这些进展包括估计 风险概率（EP）;定义为在特定条件下相对风险 位置大于1。当应用于连续空间时，EP提供了灵敏的识别 具有不同的集群边界和大小，并具有明确的，空间变化的确定性的疾病集群。 此外，将该模型扩展到多种疾病可以客观地将联合收割机疾病与常见疾病结合起来。 空间模式，从而提高有效的样本量。问题是儿童癌症非常罕见 空间参数的先验分布可能不适当地影响结果。我们最需要的是 当特定的CC具有共同的空间风险模式时，将联合收割机CC组合的客观方式。长期目标是 预防由环境暴露引起的疾病。本申请的总体目标是 我们长期目标的下一步是找到最客观的方法，当他们共享CC子组时， 共同的空间风险模式。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研究结果制定的。的基本原理 提出的研究的基础是，最近开发的贝叶斯多变量空间建模是链接 我们需要减轻空间不确定性，恢复公众对集群调查的信心。中央 将通过追求以下具体目标来检验假设并实现本申请的目标： 1.使用EP的单变量地统计建模评估单个CC的病例过剩。我们假设， 根据我们目前的研究，EP的地质统计学建模将提供更高的灵敏度， 通过允许灵活的聚类形状、大小和统计确定性来进行聚类检测。2、评估案例过剩 使用EP的多变量地质统计学建模的多CC。我们假设，根据我们的初步 多个CC在一些有毒站点附近具有共同的地理模式， 的CC将提高集群检测的灵敏度。关于预期成果， 在目标1中提出的建议将确定一些德克萨斯州超级基金站点附近的单个CC的重大风险模式。目的 2将确定这些地点具有共同地理风险模式的CC。这一贡献意义重大 因为我们生活在一个时代，在这个时代，公众的警惕性是寻求所有的环境风险和 必须鼓励和验证公众的投入，最重要的是，必须客观地处理公众的投入。的 贡献是创新的，因为拟议的研究结合了最新的进展，以解决问题， 疾病群调查中的空间不确定性建模和报告。

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.