Goovaerts Pierre
BioMedware, Inc., 3526 W Liberty, Suite 100, Ann Arbor, MI 48103, USA.
Spat Spatiotemporal Epidemiol. 2010 Dec;1(4):219-29. doi: 10.1016/j.sste.2010.09.004.
This paper presents alternative implementations of boundary analysis to: (1) quantify rate changes across boundaries both in an absolute (rate difference) and relative (rate ratio) ways, (2) detect changes of a minimum magnitude by using a null hypothesis of non-uniform risk, (3) account for spatial patterns and population sizes in the randomization procedure, and (4) compute multi-edge p-values for use in multiple testing correction. Simulation studies demonstrate that accounting for spatial autocorrelation and population size in the randomization procedure improves the discriminant power of the statistic, leading to a higher agreement between target and detected edges according to van Rijsbergen’s F-measure. The increase in power and F-measure was strongest for the tests based on the null hypothesis of non-uniform risk across the edge. Multiple testing correction slightly decreases the classification accuracy, yet it reduces substantially the proportion of false positives that becomes very close to the significance level when using the new procedure based on multi-edge p-values.
本文提出了边界分析的替代实现方法，以：（1）以绝对（速率差异）和相对（速率比）方式量化边界两侧的速率变化；（2）通过使用非均匀风险的零假设检测最小幅度的变化；（3）在随机化过程中考虑空间模式和总体大小；（4）计算用于多重检验校正的多边p值。模拟研究表明，在随机化过程中考虑空间自相关和总体大小可提高统计量的判别能力，根据范·里杰斯伯根的F度量，目标边界和检测到的边界之间的一致性更高。对于基于边缘非均匀风险零假设的检验，功效和F度量的增加最为显著。多重检验校正会略微降低分类准确率，但它会大幅降低误报比例，当使用基于多边p值的新程序时，误报比例会非常接近显著性水平。
