Power evaluation of disease clustering tests

International Journal of Health Geographics

Martin Kulldorff¹  Changhong Song² 

[1] Department ofAmbulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care,133 Brookline Avenue,6th Floor, Boston, MA 02215, USA;Department of Statistics, University of Connecticut, Storrs, Connecticut, 06269, U.S.A

关键词: test for spatial randomness;    global chain clustering;    hot spot clusters;    cluster detection;    power;    benchmark data;    Spatial statistics;   

Others  :  1149464
DOI  :  10.1186/1476-072X-2-9

 received in 2003-10-30, accepted in 2003-12-19,  发布年份 2003

【 摘 要 】

Background

Many different test statistics have been proposed to test for spatial clustering. Some of these statistics have been widely used in various applications. In this paper, we use an existing collection of 1,220,000 simulated benchmark data, generated under 51 different clustering models, to compare the statistical power of several disease clustering tests. These tests are Besag-Newell's R, Cuzick-Edwards' k-Nearest Neighbors (k-NN), the spatial scan statistic, Tango's Maximized Excess Events Test (MEET), Swartz' entropy test, Whittemore's test, Moran's I and a modification of Moran's I.

Results

Except for Moran's I and Whittemore's test, all other tests have good power for detecting some kind of clustering. The spatial scan statistic is good at detecting localized clusters. Tango's MEET is good at detecting global clustering. With appropriate choice of parameter, Besag-Newell's R and Cuzick-Edwards' k-NN also perform well.

Conclusion

The power varies greatly for different test statistics and alternative clustering models. Consideration of the power is important before we decide which test statistic to use.

【 授权许可】

   
2003 Song and Kulldorff; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.

【 参考文献 】

• [1]RuizGarcia M: Genetic relationships among some new cat populations sampled in Europe: A spatial autocorrelation analysis. Journal of Genetics 1997, 76:1-24.
• [2]Gustine DL, Elwinger GF: Spatiotemporal genetic structure within white clover populations in grazed swards. Crop Science 2003, 43:337-344.
• [3]Aubry P, Piegay H: Spatial autocorrelation analysis in geomorphology: Definitions and tests. Geographic Phisique et Quaternaire 2001, 55:111-129.
• [4]Meirmans PG, Vlot EC, Den Nijs JCM, Menken SBJ: Spatial ecological and genetic structure of a mixed population of sexual diploid and apomictic triploid dandelions. Journal of Evolutionary Biology 2003, 16:343-352.
• [5]Liebhold AM, Gurevitch J: Integrating the statistical analysis of spatial data in ecology. Ecography 2002, 25:553-557.
• [6]Clark SA, Richardson BJ: Spatial analysis of genetic variation as a rapid assessment tool in the conservation management of narrow-range endemics. Invertebrate Systematics 2002, 16:583-587.
• [7]Rogerson PA: The detection of clusters using a spatial version of the chi-square goodness-of-fit statistic. Geographical Analysis 1999, 31:130-147.
• [8]Kulldorff M, Nagarwalla N: Spatial disease clusters: Detection and inference. Statistics in Medicine 1995, 14:799-810.
• [9]Oden N: Adjusting Moran' I for population density. Statistics in Medicine 1995, 14:17-26.
• [10]Kulldorff M, Tango T, Park P: Power comparisons for disease clustering tests. Computational Statistics and Data Analysis 2003, 42:665-684.
• [11]Tango T: A class of tests for detecting 'general' and 'focused' clustering of rare diseases. Statistics in Medicine 1995, 14:2323-2334.
• [12]Tango T: A test for spatial disease clustering adjusted for multiple testing. Statistics in Medicine 2000, 19:191-204.
• [13]Vach W: Locally optimal tests on spatial clustering. In in New Approaches in Classification and Data Analysis. Edited by Diday. Berlin, Springer-Verlag; 1994:161-168.
• [14]Tango T: Comparison of general tests for spatial clustering. In In Disease Mapping and Risk Assessment for Public Health. Edited by Lawson, et al. London, Wiley; 1999:111-117.
• [15]Besag J, Newell J: The detection of clusters in rare diseases. Journal of the Royal Statistical Society 1991, A154:143-155.
• [16]Waller LA, Turnbull BW, Clark LC, Nasca P: Spatial pattern analyses to detect rare disease clusters. In Case Studies in Biometry. Edited by Lange N, Ryan L, Billard L, Brillinger D, Conquest L, Greenhourse J. New York: John Wiley & Sons; 1994:13-16.
• [17]Cuzick J, Edwards R: Spatial clustering for inhomogeneous populations. Journal of the Royal Statistical Society 1990, B52:73-104.
• [18]Dockerty JD, Sharples KJ, Borman B: An assessment of spatial clustering of leukaemias and lymphomas among young people in New Zealand. Journal of Epidemiology and Community Health 1999, 53:154-8.
• [19]Vredevoe LK, Righter PJ, Madigan JE, Kimsey RB: Association of Ixodes pacificus (Acari: Ixodidae) with the spatial and temporal distribution of equine granulocytic ehrlichiosis in California. Journal of Medical Entomology 1999, 36:551-561.
• [20]Kulldorff M: A spatial scan statistic. Communications in Statistics: Theory and Methods 1997, 26:1481-1496.
• [21]Chaput EK, Meek JI, Heimer R: Spatial analysis of human granulocytic ehrlichiosis near Lyme, Connecticut. Emerging Infectious Diseases 2002, 8:943-948.
• [22]Viel JF, Arveux P, Baverel J, Cahn JY: Soft-tissue sarcoma and non-Hodgkin's lymphoma clusters around a municipal solid waste incinerator with high dioxin emission levels. American Journal of Epidemiology 2000, 152:13-19.
• [23]Sankoh OA, Ye Y, Sauerborn R, Muller O, Becher H: Clustering of childhood mortality in rural Burkina Faso. International Journal of Epidemiology 2001, 30:485-492.
• [24]Perez AM, Ward MP, Torres P, Ritacco V: Use of spatial statistics and monitoring data to identify clustering of bovine tuberculosis in Argentina. Preventive Veterinary Medicine 2002, 56:63-74.
• [25]Miller MA, Gardner IA, Kreuder C, Paradies DM, Worcester KR, Jessup DA, Dodd E, Harris MD, Ames JA, Packham AE, Conrad PA: Coastal freshwater runoff is a risk factor for Toxoplasma gondii infection of southern sea otters (Enhydra lutris nereis). International Journal for Parasitology 2002, 32:997-1006.
• [26]Dwass M: Modified randomization tests for nonparametric hypotheses. Annals of Mathematical Statistics 1957, 28:181-187.
• [27]Swartz JB: An entropy-based algorithm for detecting clusters of cases and controls and its comparison with a method using nearest neighbors. Health and Place 1998, 4:67-77.
• [28]Kulldorff M: Letter to the editor. Health and Place 1999, 5:313.
• [29]Moran PAP: Notes on continuous stochastic phenomena. Biometrika 1950, 37:17-23.
• [30]Glavanakov S, White DJ, Caraco T, Lapenis A, Robinson GR, Szymanski BK, Maniatty WA: Lyme disease in New York State: Spatial pattern at a regional scale. American Journal of Tropical Medicine and Hygiene 2001, 65:538-545.
• [31]Le ND, Marret LD, Roberson DL, Semenciw RM, Turner D, Walter SD: Canadian Cancer Incidence Atlas. Canadian Government Publishing. 1995.
• [32]Whittemore AS, Friend N, Brown BW, Holly EA: A test to detect clusters of disease. Biometrika 1987, 74:631-635.
• [33]Bonetti M, Pagano M: On detecting clustering. Proceedings of the Biometrics Section American Statistical Association 2001, 24-33.