期刊名称：Environmental Health - a Global Access Science Source
印刷版ISSN：1476-069X
电子版ISSN：1476-069X
出版年度：2005
卷号：4
期号：1
页码：19
DOI：10.1186/1476-069X-4-19
语种：English
出版社：BioMed Central
摘要：A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM) method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component.
关键词：Generalize Additive Model ; Cold Spot ; Breast Cancer Data ; Bernoulli Model ; Interpoint Distance
