Optimizing the maximum reported cluster size in the spatial scan statistic for survival data

International Journal of Health Geographics

Jisu Moon¹  Sujee Lee¹  Inkyung Jung¹ 

[1] Division of Biostatistics, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, 03722, Seoul, Korea;

关键词: Spatial cluster detection;    Exponential model;    Gini coefficient;    SaTScan;   

DOI  :  10.1186/s12942-021-00286-w

来源: Springer

【 摘 要 】

BackgroundThe spatial scan statistic is a useful tool for cluster detection analysis in geographical disease surveillance. The method requires users to specify the maximum scanning window size or the maximum reported cluster size (MRCS), which is often set to 50% of the total population. It is important to optimize the maximum reported cluster size, keeping the maximum scanning window size at as large as 50% of the total population, to obtain valid and meaningful results.ResultsWe developed a measure, a Gini coefficient, to optimize the maximum reported cluster size for the exponential-based spatial scan statistic. The simulation study showed that the proposed method mostly selected the optimal MRCS, similar to the true cluster size. The detection accuracy was higher for the best chosen MRCS than at the default setting. The application of the method to the Korea Community Health Survey data supported that the proposed method can optimize the MRCS in spatial cluster detection analysis for survival data.ConclusionsUsing the Gini coefficient in the exponential-based spatial scan statistic can be very helpful for reporting more refined and informative clusters for survival data.

【 授权许可】

CC BY