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Tango's maximized excess events test with different weights

BACKGROUND: Tango's maximized excess events test (MEET) has been shown to have very good statistical power in detecting global disease clustering. A nice feature of this test is that it considers a range of spatial scale parameters, adjusting for the multiple testing. This means that it has goo...

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Detalles Bibliográficos
Autores principales: Song, Changhong, Kulldorff, Martin
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1343587/
https://www.ncbi.nlm.nih.gov/pubmed/16356179
http://dx.doi.org/10.1186/1476-072X-4-32
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author Song, Changhong
Kulldorff, Martin
author_facet Song, Changhong
Kulldorff, Martin
author_sort Song, Changhong
collection PubMed
description BACKGROUND: Tango's maximized excess events test (MEET) has been shown to have very good statistical power in detecting global disease clustering. A nice feature of this test is that it considers a range of spatial scale parameters, adjusting for the multiple testing. This means that it has good power to detect a wide range of clustering processes. The test depends on the functional form of a weight function, and it is unknown how sensitive the test is to the choice of this weight function and what function provides optimal power for different clustering processes. In this study, we evaluate the performance of the test for a wide range of weight functions. RESULTS: The power varies greatly with different choice of weight. Tango's original choice for the weight function works very well. There are also other weight functions that provide good power. CONCLUSION: We recommend the use of Tango's MEET to test global disease clustering, either with the original weight or one of the alternate weights that have good power.
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spelling pubmed-13435872006-01-22 Tango's maximized excess events test with different weights Song, Changhong Kulldorff, Martin Int J Health Geogr Research BACKGROUND: Tango's maximized excess events test (MEET) has been shown to have very good statistical power in detecting global disease clustering. A nice feature of this test is that it considers a range of spatial scale parameters, adjusting for the multiple testing. This means that it has good power to detect a wide range of clustering processes. The test depends on the functional form of a weight function, and it is unknown how sensitive the test is to the choice of this weight function and what function provides optimal power for different clustering processes. In this study, we evaluate the performance of the test for a wide range of weight functions. RESULTS: The power varies greatly with different choice of weight. Tango's original choice for the weight function works very well. There are also other weight functions that provide good power. CONCLUSION: We recommend the use of Tango's MEET to test global disease clustering, either with the original weight or one of the alternate weights that have good power. BioMed Central 2005-12-15 /pmc/articles/PMC1343587/ /pubmed/16356179 http://dx.doi.org/10.1186/1476-072X-4-32 Text en Copyright © 2005 Song and Kulldorff; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Song, Changhong
Kulldorff, Martin
Tango's maximized excess events test with different weights
title Tango's maximized excess events test with different weights
title_full Tango's maximized excess events test with different weights
title_fullStr Tango's maximized excess events test with different weights
title_full_unstemmed Tango's maximized excess events test with different weights
title_short Tango's maximized excess events test with different weights
title_sort tango's maximized excess events test with different weights
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1343587/
https://www.ncbi.nlm.nih.gov/pubmed/16356179
http://dx.doi.org/10.1186/1476-072X-4-32
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