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Performance map of a cluster detection test using extended power
BACKGROUND: Conventional power studies possess limited ability to assess the performance of cluster detection tests. In particular, they cannot evaluate the accuracy of the cluster location, which is essential in such assessments. Furthermore, they usually estimate power for one or a few particular...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4016504/ https://www.ncbi.nlm.nih.gov/pubmed/24156765 http://dx.doi.org/10.1186/1476-072X-12-47 |
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author | Guttmann, Aline Ouchchane, Lemlih Li, Xinran Perthus, Isabelle Gaudart, Jean Demongeot, Jacques Boire, Jean-Yves |
author_facet | Guttmann, Aline Ouchchane, Lemlih Li, Xinran Perthus, Isabelle Gaudart, Jean Demongeot, Jacques Boire, Jean-Yves |
author_sort | Guttmann, Aline |
collection | PubMed |
description | BACKGROUND: Conventional power studies possess limited ability to assess the performance of cluster detection tests. In particular, they cannot evaluate the accuracy of the cluster location, which is essential in such assessments. Furthermore, they usually estimate power for one or a few particular alternative hypotheses and thus cannot assess performance over an entire region. Takahashi and Tango developed the concept of extended power that indicates both the rate of null hypothesis rejection and the accuracy of the cluster location. We propose a systematic assessment method, using here extended power, to produce a map showing the performance of cluster detection tests over an entire region. METHODS: To explore the behavior of a cluster detection test on identical cluster types at any possible location, we successively applied four different spatial and epidemiological parameters. These parameters determined four cluster collections, each covering the entire study region. We simulated 1,000 datasets for each cluster and analyzed them with Kulldorff’s spatial scan statistic. From the area under the extended power curve, we constructed a map for each parameter set showing the performance of the test across the entire region. RESULTS: Consistent with previous studies, the performance of the spatial scan statistic increased with the baseline incidence of disease, the size of the at-risk population and the strength of the cluster (i.e., the relative risk). Performance was heterogeneous, however, even for very similar clusters (i.e., similar with respect to the aforementioned factors), suggesting the influence of other factors. CONCLUSIONS: The area under the extended power curve is a single measure of performance and, although needing further exploration, it is suitable to conduct a systematic spatial evaluation of performance. The performance map we propose enables epidemiologists to assess cluster detection tests across an entire study region. |
format | Online Article Text |
id | pubmed-4016504 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-40165042014-05-23 Performance map of a cluster detection test using extended power Guttmann, Aline Ouchchane, Lemlih Li, Xinran Perthus, Isabelle Gaudart, Jean Demongeot, Jacques Boire, Jean-Yves Int J Health Geogr Methodology BACKGROUND: Conventional power studies possess limited ability to assess the performance of cluster detection tests. In particular, they cannot evaluate the accuracy of the cluster location, which is essential in such assessments. Furthermore, they usually estimate power for one or a few particular alternative hypotheses and thus cannot assess performance over an entire region. Takahashi and Tango developed the concept of extended power that indicates both the rate of null hypothesis rejection and the accuracy of the cluster location. We propose a systematic assessment method, using here extended power, to produce a map showing the performance of cluster detection tests over an entire region. METHODS: To explore the behavior of a cluster detection test on identical cluster types at any possible location, we successively applied four different spatial and epidemiological parameters. These parameters determined four cluster collections, each covering the entire study region. We simulated 1,000 datasets for each cluster and analyzed them with Kulldorff’s spatial scan statistic. From the area under the extended power curve, we constructed a map for each parameter set showing the performance of the test across the entire region. RESULTS: Consistent with previous studies, the performance of the spatial scan statistic increased with the baseline incidence of disease, the size of the at-risk population and the strength of the cluster (i.e., the relative risk). Performance was heterogeneous, however, even for very similar clusters (i.e., similar with respect to the aforementioned factors), suggesting the influence of other factors. CONCLUSIONS: The area under the extended power curve is a single measure of performance and, although needing further exploration, it is suitable to conduct a systematic spatial evaluation of performance. The performance map we propose enables epidemiologists to assess cluster detection tests across an entire study region. BioMed Central 2013-10-25 /pmc/articles/PMC4016504/ /pubmed/24156765 http://dx.doi.org/10.1186/1476-072X-12-47 Text en Copyright © 2013 Guttmann et al.; 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 | Methodology Guttmann, Aline Ouchchane, Lemlih Li, Xinran Perthus, Isabelle Gaudart, Jean Demongeot, Jacques Boire, Jean-Yves Performance map of a cluster detection test using extended power |
title | Performance map of a cluster detection test using extended power |
title_full | Performance map of a cluster detection test using extended power |
title_fullStr | Performance map of a cluster detection test using extended power |
title_full_unstemmed | Performance map of a cluster detection test using extended power |
title_short | Performance map of a cluster detection test using extended power |
title_sort | performance map of a cluster detection test using extended power |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4016504/ https://www.ncbi.nlm.nih.gov/pubmed/24156765 http://dx.doi.org/10.1186/1476-072X-12-47 |
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