Cancer epidemiology studies require sufficient power to assess spatial relationships between exposures and cancer incidence accurately. However, methods for power calculations of spatial statistics are complicated and underdeveloped, and therefore underutilized by investigators. The spatial relative risk function, a cluster detection technique that detects spatial clusters of point-level data for two groups (e.g., cancer cases and controls, two exposure groups), is a commonly used spatial statistic but does not have a readily available power calculation for study design. We developed sparrpowR as an open-source R package to estimate the statistical power of the spatial relative risk function. sparrpowR generates simulated data applying user-defined parameters (e.g., sample size, locations) to detect spatial clusters with high statistical power. We present applications of sparrpowR that perform a power calculation for a study designed to detect a spatial cluster of incident cancer in relation to a point source of numerous environmental emissions. The conducted power calculations demonstrate the functionality and utility of sparrpowR to calculate the local power for spatial cluster detection. sparrpowR improves the current capacity of investigators to calculate the statistical power of spatial clusters, which assists in designing more efficient studies. This newly developed R package addresses a critically underdeveloped gap in cancer epidemiology by estimating statistical power for a common spatial cluster detection technique.

中文翻译：

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sparrpowR：灵活的R包，用于估计统计能力以识别两组的空间聚类及其应用

癌症流行病学研究需要足够的能力来准确评估暴露与癌症发生率之间的空间关系。但是，用于空间统计量的幂计算的方法复杂且开发不完善，因此调查人员未充分利用。空间相对风险函数是一种群集检测技术，可检测两组（例如，癌症病例和对照组，两个暴露组）的点级数据的空间群集，是一种常用的空间统计量，但尚不具备可用的功效计算功能用于研究设计。我们开发了sparrpowR作为开源R包，以估计空间相对风险函数的统计能力。sparrpowR使用用户定义的参数（例如，样本大小，位置）生成模拟数据，以检测具有高统计能力的空间聚类。我们介绍了sparrpowR的应用程序，该应用程序为一项旨在检测与大量环境排放物的点源相关的事件性癌症的空间簇的研究进行功率计算。进行的功率计算证明了sparrpowR的功能和效用，可用于计算空间聚类检测的局部功率。sparrpowR提高了调查人员计算空间聚类统计能力的当前能力，这有助于设计更有效的研究。这个新开发的R包通过估计常见空间聚类检测技术的统计能力来解决癌症流行病学方面严重欠发达的空白。