政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/54409

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/54409

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政大機構典藏 > 商學院 > 統計學系 > 學位論文 > Item 140.119/54409

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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:	余清祥蔡紋琦 Jack C. Yue 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:	2012-10-30 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, 13:1341-1352 Diggle, 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. 69-74 Jackson, 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 Press Lloyd, .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
顯示於類別:	[統計學系] 學位論文

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