Flexible scan statistic test to detect disease clusters in hierarchical trees
Abstract This paper presents a flexible scan test statistic to detect disease clusters in data sets represented as a hierarchical tree. The algorithm searches through the branches of the tree and it is able to aggregate leaves located in different branches. The test statistic combines two terms, the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Prates, Marcos O. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2011 |
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| Übergeordnetes Werk: |
Enthalten in: Computational statistics - Berlin : Springer, 1999, 27(2011), 4 vom: 18. Okt., Seite 715-737 |
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| Übergeordnetes Werk: |
volume:27 ; year:2011 ; number:4 ; day:18 ; month:10 ; pages:715-737 |
| Links: |
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| DOI / URN: |
10.1007/s00180-011-0286-9 |
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| Katalog-ID: |
SPR001513974 |
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| 100 | 1 | |a Prates, Marcos O. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Flexible scan statistic test to detect disease clusters in hierarchical trees |
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| 520 | |a Abstract This paper presents a flexible scan test statistic to detect disease clusters in data sets represented as a hierarchical tree. The algorithm searches through the branches of the tree and it is able to aggregate leaves located in different branches. The test statistic combines two terms, the log-likelihood of the data and the amount of information necessary to computationally code each potential cluster. This second term penalizes the search algorithm avoiding the detection of oddly shaped clusters and it is based on the Minimum Description Length (MDL) principle. Our MDL method reaches an automatic compromise between bias and variance. We present simulated results showing that its power performance as compared to the usual scan statistic and the high accuracy of the MDL to identify clusters that are scattered on the tree. The MDL method is illustrated with a large database looking at the relationship between occupation and death from silicosis. | ||
| 650 | 4 | |a Cluster detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Data mining |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Exploratory analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Hierarchical tree |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Scan statistics |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Assunção, Renato M. |4 aut | |
| 700 | 1 | |a Costa, Marcelo A. |4 aut | |
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10.1007/s00180-011-0286-9 doi (DE-627)SPR001513974 (SPR)s00180-011-0286-9-e DE-627 ger DE-627 rakwb eng Prates, Marcos O. verfasserin aut Flexible scan statistic test to detect disease clusters in hierarchical trees 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract This paper presents a flexible scan test statistic to detect disease clusters in data sets represented as a hierarchical tree. The algorithm searches through the branches of the tree and it is able to aggregate leaves located in different branches. The test statistic combines two terms, the log-likelihood of the data and the amount of information necessary to computationally code each potential cluster. This second term penalizes the search algorithm avoiding the detection of oddly shaped clusters and it is based on the Minimum Description Length (MDL) principle. Our MDL method reaches an automatic compromise between bias and variance. We present simulated results showing that its power performance as compared to the usual scan statistic and the high accuracy of the MDL to identify clusters that are scattered on the tree. The MDL method is illustrated with a large database looking at the relationship between occupation and death from silicosis. Cluster detection (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Exploratory analysis (dpeaa)DE-He213 Hierarchical tree (dpeaa)DE-He213 Scan statistics (dpeaa)DE-He213 Assunção, Renato M. aut Costa, Marcelo A. aut Enthalten in Computational statistics Berlin : Springer, 1999 27(2011), 4 vom: 18. Okt., Seite 715-737 (DE-627)271599170 (DE-600)1480911-4 1613-9658 nnns volume:27 year:2011 number:4 day:18 month:10 pages:715-737 https://dx.doi.org/10.1007/s00180-011-0286-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2011 4 18 10 715-737 |
| spelling |
10.1007/s00180-011-0286-9 doi (DE-627)SPR001513974 (SPR)s00180-011-0286-9-e DE-627 ger DE-627 rakwb eng Prates, Marcos O. verfasserin aut Flexible scan statistic test to detect disease clusters in hierarchical trees 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract This paper presents a flexible scan test statistic to detect disease clusters in data sets represented as a hierarchical tree. The algorithm searches through the branches of the tree and it is able to aggregate leaves located in different branches. The test statistic combines two terms, the log-likelihood of the data and the amount of information necessary to computationally code each potential cluster. This second term penalizes the search algorithm avoiding the detection of oddly shaped clusters and it is based on the Minimum Description Length (MDL) principle. Our MDL method reaches an automatic compromise between bias and variance. We present simulated results showing that its power performance as compared to the usual scan statistic and the high accuracy of the MDL to identify clusters that are scattered on the tree. The MDL method is illustrated with a large database looking at the relationship between occupation and death from silicosis. Cluster detection (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Exploratory analysis (dpeaa)DE-He213 Hierarchical tree (dpeaa)DE-He213 Scan statistics (dpeaa)DE-He213 Assunção, Renato M. aut Costa, Marcelo A. aut Enthalten in Computational statistics Berlin : Springer, 1999 27(2011), 4 vom: 18. Okt., Seite 715-737 (DE-627)271599170 (DE-600)1480911-4 1613-9658 nnns volume:27 year:2011 number:4 day:18 month:10 pages:715-737 https://dx.doi.org/10.1007/s00180-011-0286-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2011 4 18 10 715-737 |
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10.1007/s00180-011-0286-9 doi (DE-627)SPR001513974 (SPR)s00180-011-0286-9-e DE-627 ger DE-627 rakwb eng Prates, Marcos O. verfasserin aut Flexible scan statistic test to detect disease clusters in hierarchical trees 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract This paper presents a flexible scan test statistic to detect disease clusters in data sets represented as a hierarchical tree. The algorithm searches through the branches of the tree and it is able to aggregate leaves located in different branches. The test statistic combines two terms, the log-likelihood of the data and the amount of information necessary to computationally code each potential cluster. This second term penalizes the search algorithm avoiding the detection of oddly shaped clusters and it is based on the Minimum Description Length (MDL) principle. Our MDL method reaches an automatic compromise between bias and variance. We present simulated results showing that its power performance as compared to the usual scan statistic and the high accuracy of the MDL to identify clusters that are scattered on the tree. The MDL method is illustrated with a large database looking at the relationship between occupation and death from silicosis. Cluster detection (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Exploratory analysis (dpeaa)DE-He213 Hierarchical tree (dpeaa)DE-He213 Scan statistics (dpeaa)DE-He213 Assunção, Renato M. aut Costa, Marcelo A. aut Enthalten in Computational statistics Berlin : Springer, 1999 27(2011), 4 vom: 18. Okt., Seite 715-737 (DE-627)271599170 (DE-600)1480911-4 1613-9658 nnns volume:27 year:2011 number:4 day:18 month:10 pages:715-737 https://dx.doi.org/10.1007/s00180-011-0286-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2011 4 18 10 715-737 |
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10.1007/s00180-011-0286-9 doi (DE-627)SPR001513974 (SPR)s00180-011-0286-9-e DE-627 ger DE-627 rakwb eng Prates, Marcos O. verfasserin aut Flexible scan statistic test to detect disease clusters in hierarchical trees 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract This paper presents a flexible scan test statistic to detect disease clusters in data sets represented as a hierarchical tree. The algorithm searches through the branches of the tree and it is able to aggregate leaves located in different branches. The test statistic combines two terms, the log-likelihood of the data and the amount of information necessary to computationally code each potential cluster. This second term penalizes the search algorithm avoiding the detection of oddly shaped clusters and it is based on the Minimum Description Length (MDL) principle. Our MDL method reaches an automatic compromise between bias and variance. We present simulated results showing that its power performance as compared to the usual scan statistic and the high accuracy of the MDL to identify clusters that are scattered on the tree. The MDL method is illustrated with a large database looking at the relationship between occupation and death from silicosis. Cluster detection (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Exploratory analysis (dpeaa)DE-He213 Hierarchical tree (dpeaa)DE-He213 Scan statistics (dpeaa)DE-He213 Assunção, Renato M. aut Costa, Marcelo A. aut Enthalten in Computational statistics Berlin : Springer, 1999 27(2011), 4 vom: 18. Okt., Seite 715-737 (DE-627)271599170 (DE-600)1480911-4 1613-9658 nnns volume:27 year:2011 number:4 day:18 month:10 pages:715-737 https://dx.doi.org/10.1007/s00180-011-0286-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2011 4 18 10 715-737 |
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| author |
Prates, Marcos O. |
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Prates, Marcos O. misc Cluster detection misc Data mining misc Exploratory analysis misc Hierarchical tree misc Scan statistics Flexible scan statistic test to detect disease clusters in hierarchical trees |
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Flexible scan statistic test to detect disease clusters in hierarchical trees Cluster detection (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Exploratory analysis (dpeaa)DE-He213 Hierarchical tree (dpeaa)DE-He213 Scan statistics (dpeaa)DE-He213 |
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Flexible scan statistic test to detect disease clusters in hierarchical trees |
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Prates, Marcos O. Assunção, Renato M. Costa, Marcelo A. |
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flexible scan statistic test to detect disease clusters in hierarchical trees |
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Flexible scan statistic test to detect disease clusters in hierarchical trees |
| abstract |
Abstract This paper presents a flexible scan test statistic to detect disease clusters in data sets represented as a hierarchical tree. The algorithm searches through the branches of the tree and it is able to aggregate leaves located in different branches. The test statistic combines two terms, the log-likelihood of the data and the amount of information necessary to computationally code each potential cluster. This second term penalizes the search algorithm avoiding the detection of oddly shaped clusters and it is based on the Minimum Description Length (MDL) principle. Our MDL method reaches an automatic compromise between bias and variance. We present simulated results showing that its power performance as compared to the usual scan statistic and the high accuracy of the MDL to identify clusters that are scattered on the tree. The MDL method is illustrated with a large database looking at the relationship between occupation and death from silicosis. © Springer-Verlag 2011 |
| abstractGer |
Abstract This paper presents a flexible scan test statistic to detect disease clusters in data sets represented as a hierarchical tree. The algorithm searches through the branches of the tree and it is able to aggregate leaves located in different branches. The test statistic combines two terms, the log-likelihood of the data and the amount of information necessary to computationally code each potential cluster. This second term penalizes the search algorithm avoiding the detection of oddly shaped clusters and it is based on the Minimum Description Length (MDL) principle. Our MDL method reaches an automatic compromise between bias and variance. We present simulated results showing that its power performance as compared to the usual scan statistic and the high accuracy of the MDL to identify clusters that are scattered on the tree. The MDL method is illustrated with a large database looking at the relationship between occupation and death from silicosis. © Springer-Verlag 2011 |
| abstract_unstemmed |
Abstract This paper presents a flexible scan test statistic to detect disease clusters in data sets represented as a hierarchical tree. The algorithm searches through the branches of the tree and it is able to aggregate leaves located in different branches. The test statistic combines two terms, the log-likelihood of the data and the amount of information necessary to computationally code each potential cluster. This second term penalizes the search algorithm avoiding the detection of oddly shaped clusters and it is based on the Minimum Description Length (MDL) principle. Our MDL method reaches an automatic compromise between bias and variance. We present simulated results showing that its power performance as compared to the usual scan statistic and the high accuracy of the MDL to identify clusters that are scattered on the tree. The MDL method is illustrated with a large database looking at the relationship between occupation and death from silicosis. © Springer-Verlag 2011 |
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Flexible scan statistic test to detect disease clusters in hierarchical trees |
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https://dx.doi.org/10.1007/s00180-011-0286-9 |
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Assunção, Renato M. Costa, Marcelo A. |
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Assunção, Renato M. Costa, Marcelo A. |
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| doi_str |
10.1007/s00180-011-0286-9 |
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2024-07-03T23:03:28.758Z |
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| score |
7.3993683 |