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題名 多重群集的偵測研究
A study of methods for detecting multiple clusters作者 黃柏誠
Huang, Bo Cheng貢獻者 余清祥<br>蔡紋琦
Jack C. Yue<br>Wun-Ci Cai
黃柏誠
Huang, Bo Cheng關鍵詞 群集偵測
空間統計
逐次分析
電腦模擬
Cluster detection
Spatial statistics
Sequential method
Computer simulation日期 2011 上傳時間 30-Oct-2012 10:58:20 (UTC+8) 摘要 檢測某些地區是否有較高的疾病發生率，亦即群集(Cluster)現象，是近年來空間統計(Spatial Statistics)在流行病學的主要應用之一，常見的偵測方法包括SaTScan (Kulldorff, 1995)及Spatial Scan Statistic (Li et al., 2011)。這些方法多半大都採用一次性偵測，也就是比較疑似群集之內外相對風險(Relative Risk)，如此確實可提高計算效率，同時檢視所有疑似群集。然而，一次性偵測會受到群集外其他發生率較高群集的影響，對於相對風險較小群集的偵測能力過於保守(Zhang et al., 2010)。本文以多重群集偵測為研究目標，以逐次分析的方式修正SaTScan等群集偵測方法，逐一篩選出發生率較高的顯著群集，並探討逐次分析在使用上的時機及限制。除了透過電腦模擬，測試逐次群集分析的改進效果，我們也分析臺灣地區的癌症死亡率，比較偵測結果的差異。研究發現，逐次群集偵測確實能提高相對風險較小群集的偵測能力，像是在相對風險不大於1.6的群集時尤其有效，但若相對風險大於1.6時，SaTScan的偵測能力不受多重群集的影響。
Cluster detection, one of the major research topics in spatial statistics, has been applied to identify areas with higher incidence rates and is very popular in many fields such as epidemiology. Many famous cluster detection methods are proposed, such as SaTScan (Kulldorff, 1995) and Spatial Scan Statistic (Li et al., 2011). Most of these methods adapt the idea for comparing the relative risk inside and outside the suspected clusters. Although these methods are efficient computationally, clusters with smaller relative risk are not easy to be detected (Zhang et al, 2010). The goal of this study is to apply the idea of sequential search into SaTScan, in order to improve the power of detecting clusters with smaller relative risk, and to explore the limitation of sequential method. The computer simulation and empirical study (Taiwan cancer mortality data) are used to evaluate the sequential SaTScan. We found that the Sequential method can improve the power of cluster detection, especially effective for the cases where the clusters with relative risk not greater than 1.6. However, the sequential method also suffers from identifying false clusters.參考文獻 Auchincloss, A.H., Gebreab, S.Y., Mair, C. and Diez Roux, A.V. (2012). A Review of Spatial Methods in Epidemiology, 2000–2010, Annual Review of Public Health, 33:107–22 Bithell J.F. (1995). The choice of test for detecting raised disease risk near a point source, Statistics in Medicine 14:2309–2322. Cliff, A. and Ord, J.K. (1981). Spatial Processes: Model and Applications, London: Pion. Cucala, L. (2009). A flexible spatial scan test for case event data, Computational Statistics and Data Analysis 53: 2843–2850. Demattei, C., Molinari, N. and Daures, J.P. (2007). Arbitrarily shaped multiple spatial cluster detection for case event data, Computational Statistics and Data Analysis 51:3931–3945. DeMets, DL and Lan, KKG (1994). Interim analysis: The alpha spending function approach. Statistics in Medicine, 13:1341-1352Diggle, P.J. (1990). A point process modelling approach to raised incidence of a rare phenomenon in the vicinity of a prespecified point, Journal of the Royal Statistical Society 153:349-362. Diggle, P.J. and Rowlinson, B.S. (1994). A conditional approach to point process modeling of elevated risk, Journal of the Royal Statistical Society 157:433-440. Fairbanks, K. and Madsen, R. (1982). P values for tests using a repeated significance test design, Biometrilca, 69, 1, pp. 69-74Jackson, M.C., Huang, L., Luo, J., Hachey, M. and Feuer, E. (2009). Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers, International Journal of Health Geographics 8:55. Kulldorff M. and Nagarwalla, N. (1995). Spatial disease clusters: detection and inference, Statistics in Medicine 14: 799–810. Kulldorff, M., Huang, L., Pickle, L. and Duczmal, L. (2006). An elliptic spatial scan statistic, Statistics in Medicine 25: 3929–3943. Kulldorff, M., Tango, T. and Park, P.J. (2003). Power comparisons for disease clustering tests, Computational Statistics and Data Analysis 42: 665–684.Li, X-Z, Wang, J-F, Yang, W-Z, Li, Z-J and Lai, S-J. (2010). A spatial scan statistic for multiple clusters, Mathematical Biosciences, 233: 135–142. Lilienfeld, D.E. and Stolley, P.D. (1994). Foundations of Epidemiology (3rd Ed.). Oxford University PressLloyd, .N, Trefethen and David, .Bau, III. (1997). Numerical Linear Algebra, SIAM Song, C. and Kulldorff, M. (2003). Power evaluation of disease clustering tests, International Journal of Health Geographics 2. Song, C. and Kulldorff, M. (2005). Tango`s maximized excess events test with different weights, International Journal of Health Geographics Dec 15: 4:32. Stone R.A. (1988). Investigations of excess environmental risks around putative sources: statistical problems and a proposed test, Statistics in Medicine 7:649–660. Tango, T. (1995). A class of tests for detecting general and focused clustering of rare diseases, Statistics in Medicine 14: 2323-2334. Tango, T. (2000). A test for spatial disease clustering adjusted for multiple testing, Statistics in Medicine 19:191-204. Tango, T. and Takahashi, K. (2005). A flexibly shaped spatial scan statistic for detecting clusters, International Journal of Health Geographics 4. Waldhor T. (1996). The spatial autocorrelation coefficient Moran`s I under heteroscedasticity, Statistics in Medicine 15(7-9)：887-892. Wan, Y., Pei, T., Zhou, C., Jiang Y., Qu, C. and Qiao, Y. (2012). ACOMCD: A multiple cluster detection algorithm based on the spatial scan statistic and ant colony optimization, Computational Statistics and Data Analysis 56：283–296.Zhang, Z., Assunção, R. and Kulldorff, M. (2010). Spatial scan statistics adjusted for multiple clusters, Journal of Probability and Statistics Article ID: 642379.王泰期, 2006。 疾病群集檢測方法及檢定力比較,政治大學碩士論文蔡承庭, 2011。 焦點檢定方法比較,政治大學碩士論文 描述 碩士
國立政治大學
統計研究所
99354013
100資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099354013 資料類型 thesis dc.contributor.advisor 余清祥<br>蔡紋琦 zh_TW dc.contributor.advisor Jack C. Yue<br>Wun-Ci Cai en_US dc.contributor.author (Authors) 黃柏誠 zh_TW dc.contributor.author (Authors) Huang, Bo Cheng en_US dc.creator (作者) 黃柏誠 zh_TW dc.creator (作者) Huang, Bo Cheng en_US dc.date (日期) 2011 en_US dc.date.accessioned 30-Oct-2012 10:58:20 (UTC+8) - dc.date.available 30-Oct-2012 10:58:20 (UTC+8) - dc.date.issued (上傳時間) 30-Oct-2012 10:58:20 (UTC+8) - dc.identifier (Other Identifiers) G0099354013 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54409 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 統計研究所 zh_TW dc.description (描述) 99354013 zh_TW dc.description (描述) 100 zh_TW dc.description.abstract (摘要) 檢測某些地區是否有較高的疾病發生率，亦即群集(Cluster)現象，是近年來空間統計(Spatial Statistics)在流行病學的主要應用之一，常見的偵測方法包括SaTScan (Kulldorff, 1995)及Spatial Scan Statistic (Li et al., 2011)。這些方法多半大都採用一次性偵測，也就是比較疑似群集之內外相對風險(Relative Risk)，如此確實可提高計算效率，同時檢視所有疑似群集。然而，一次性偵測會受到群集外其他發生率較高群集的影響，對於相對風險較小群集的偵測能力過於保守(Zhang et al., 2010)。本文以多重群集偵測為研究目標，以逐次分析的方式修正SaTScan等群集偵測方法，逐一篩選出發生率較高的顯著群集，並探討逐次分析在使用上的時機及限制。除了透過電腦模擬，測試逐次群集分析的改進效果，我們也分析臺灣地區的癌症死亡率，比較偵測結果的差異。研究發現，逐次群集偵測確實能提高相對風險較小群集的偵測能力，像是在相對風險不大於1.6的群集時尤其有效，但若相對風險大於1.6時，SaTScan的偵測能力不受多重群集的影響。 zh_TW dc.description.abstract (摘要) Cluster detection, one of the major research topics in spatial statistics, has been applied to identify areas with higher incidence rates and is very popular in many fields such as epidemiology. Many famous cluster detection methods are proposed, such as SaTScan (Kulldorff, 1995) and Spatial Scan Statistic (Li et al., 2011). Most of these methods adapt the idea for comparing the relative risk inside and outside the suspected clusters. Although these methods are efficient computationally, clusters with smaller relative risk are not easy to be detected (Zhang et al, 2010). The goal of this study is to apply the idea of sequential search into SaTScan, in order to improve the power of detecting clusters with smaller relative risk, and to explore the limitation of sequential method. The computer simulation and empirical study (Taiwan cancer mortality data) are used to evaluate the sequential SaTScan. We found that the Sequential method can improve the power of cluster detection, especially effective for the cases where the clusters with relative risk not greater than 1.6. However, the sequential method also suffers from identifying false clusters. en_US dc.description.tableofcontents 謝辭 i中文摘要 i英文摘要 ii目錄 iii表目錄 iv圖目錄 iv第一章 緒論 1第一節 研究動機 1第二節 研究目的 2第二章 文獻探討 4第一節 群集檢測方法 4(一) 總體檢定 4(二) 局部檢定 5(三) 焦點檢定 6第二節 多重群集檢測方法 7第三章 研究方法及假設 9第一節 空間統計模型 9第二節 逐次分析 10第三節 本文研究特色 10第四章 電腦模擬比較分析 12第一節 偵測結果的衡量方式 12第二節 群集所占研究區域面積比例 14(一)單一群集 15(二)兩個群集 16(三)模擬小結 20第三節 群集個數 21第四節 模擬結論與建議 24第五章 實證分析 26第一節 實證資料介紹(台灣鄉鎮市區分布) 26第二節 99年癌症死亡率分析結果 29第三節 實證應用小結 33第六章 結論與建議 35第一節 結論 35第二節 討論及建議 36參考文獻 38 zh_TW dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099354013 en_US dc.subject (關鍵詞) 群集偵測 zh_TW dc.subject (關鍵詞) 空間統計 zh_TW dc.subject (關鍵詞) 逐次分析 zh_TW dc.subject (關鍵詞) 電腦模擬 zh_TW dc.subject (關鍵詞) Cluster detection en_US dc.subject (關鍵詞) Spatial statistics en_US dc.subject (關鍵詞) Sequential method en_US dc.subject (關鍵詞) Computer simulation en_US dc.title (題名) 多重群集的偵測研究 zh_TW dc.title (題名) A study of methods for detecting multiple clusters en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) Auchincloss, A.H., Gebreab, S.Y., Mair, C. and Diez Roux, A.V. (2012). A Review of Spatial Methods in Epidemiology, 2000–2010, Annual Review of Public Health, 33:107–22 Bithell J.F. (1995). The choice of test for detecting raised disease risk near a point source, Statistics in Medicine 14:2309–2322. Cliff, A. and Ord, J.K. (1981). Spatial Processes: Model and Applications, London: Pion. Cucala, L. (2009). A flexible spatial scan test for case event data, Computational Statistics and Data Analysis 53: 2843–2850. Demattei, C., Molinari, N. and Daures, J.P. (2007). Arbitrarily shaped multiple spatial cluster detection for case event data, Computational Statistics and Data Analysis 51:3931–3945. DeMets, DL and Lan, KKG (1994). Interim analysis: The alpha spending function approach. Statistics in Medicine, 13:1341-1352Diggle, P.J. (1990). A point process modelling approach to raised incidence of a rare phenomenon in the vicinity of a prespecified point, Journal of the Royal Statistical Society 153:349-362. Diggle, P.J. and Rowlinson, B.S. (1994). A conditional approach to point process modeling of elevated risk, Journal of the Royal Statistical Society 157:433-440. Fairbanks, K. and Madsen, R. (1982). P values for tests using a repeated significance test design, Biometrilca, 69, 1, pp. 69-74Jackson, M.C., Huang, L., Luo, J., Hachey, M. and Feuer, E. (2009). Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers, International Journal of Health Geographics 8:55. Kulldorff M. and Nagarwalla, N. (1995). Spatial disease clusters: detection and inference, Statistics in Medicine 14: 799–810. Kulldorff, M., Huang, L., Pickle, L. and Duczmal, L. (2006). An elliptic spatial scan statistic, Statistics in Medicine 25: 3929–3943. Kulldorff, M., Tango, T. and Park, P.J. (2003). Power comparisons for disease clustering tests, Computational Statistics and Data Analysis 42: 665–684.Li, X-Z, Wang, J-F, Yang, W-Z, Li, Z-J and Lai, S-J. (2010). A spatial scan statistic for multiple clusters, Mathematical Biosciences, 233: 135–142. Lilienfeld, D.E. and Stolley, P.D. (1994). Foundations of Epidemiology (3rd Ed.). Oxford University PressLloyd, .N, Trefethen and David, .Bau, III. (1997). Numerical Linear Algebra, SIAM Song, C. and Kulldorff, M. (2003). Power evaluation of disease clustering tests, International Journal of Health Geographics 2. Song, C. and Kulldorff, M. (2005). Tango`s maximized excess events test with different weights, International Journal of Health Geographics Dec 15: 4:32. Stone R.A. (1988). Investigations of excess environmental risks around putative sources: statistical problems and a proposed test, Statistics in Medicine 7:649–660. Tango, T. (1995). A class of tests for detecting general and focused clustering of rare diseases, Statistics in Medicine 14: 2323-2334. Tango, T. (2000). A test for spatial disease clustering adjusted for multiple testing, Statistics in Medicine 19:191-204. Tango, T. and Takahashi, K. (2005). A flexibly shaped spatial scan statistic for detecting clusters, International Journal of Health Geographics 4. Waldhor T. (1996). The spatial autocorrelation coefficient Moran`s I under heteroscedasticity, Statistics in Medicine 15(7-9)：887-892. Wan, Y., Pei, T., Zhou, C., Jiang Y., Qu, C. and Qiao, Y. (2012). ACOMCD: A multiple cluster detection algorithm based on the spatial scan statistic and ant colony optimization, Computational Statistics and Data Analysis 56：283–296.Zhang, Z., Assunção, R. and Kulldorff, M. (2010). Spatial scan statistics adjusted for multiple clusters, Journal of Probability and Statistics Article ID: 642379.王泰期, 2006。 疾病群集檢測方法及檢定力比較,政治大學碩士論文蔡承庭, 2011。 焦點檢定方法比較,政治大學碩士論文 zh_TW
