| dc.contributor.advisor | 余清祥 | zh_TW |
| dc.contributor.author (Authors) | 王泰期 | zh_TW |
| dc.contributor.author (Authors) | Wang, Tai-Ci | en_US |
| dc.creator (作者) | 王泰期 | zh_TW |
| dc.creator (作者) | Wang, Tai-Ci | en_US |
| dc.date (日期) | 2005 | en_US |
| dc.date.accessioned | 17-Sep-2009 18:49:10 (UTC+8) | - |
| dc.date.available | 17-Sep-2009 18:49:10 (UTC+8) | - |
| dc.date.issued (上傳時間) | 17-Sep-2009 18:49:10 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0933540041 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/33923 | - |
| 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) | 933540041 | zh_TW |
| dc.description (描述) | 94 | zh_TW |
| dc.description.abstract (摘要) | 空間群聚分析應用於流行病學已行之有年，但國內這方面的研究仍較缺乏，尤其在找出哪些地區有較高疾病發生率的群聚偵測。本文針對台灣鄉鎮市資料的特性，提出一套合適的群聚檢測方法，這個方法使用兩階段的電腦模擬，實證上更容易使用；這個方法除了可找出最大顯著群聚外，也能夠偵測出多個群聚的分佈。本文使用電腦模擬比較本文的方法與目前使用較為廣泛的方法(包括Kulldorff(1995)的spatial scan statistic和Tango(2005)的flexible scan statistic)，以型一誤差、型二誤差及錯誤率三種標準衡量方法的優劣。最後套用台灣癌症死亡率與健保就診次數資料，探討台灣癌症空間群聚與就診情形的變化。 | zh_TW |
| dc.description.abstract (摘要) | Spatial cluster analyses have applied in epidemiology for many years. In this topic there still are few researches in Taiwan, especially in detecting the areas which have higher disease intensity. In this paper, we proposed a new cluster detection method which is aimed at Taiwan counties’ data. This method which uses two-stage computer simulation procedures is useful in practice. This method can find the most likely cluster. Besides, it can find multiple clusters. We use computer simulations to compare our method with others (Kulldorff’s spatial scan statistic& Tango’s flexible scan statistic). Type-I error, Type-II error and error rate are criterions of measurement. At last, we use Taiwan cancer mortality data and all the people health insurance data to discuss Taiwan cancer spatial clusters and the change of diagnoses. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1第一節 研究動機 1第二節 研究目的 1第三節 本文編排 2第二章 文獻回顧 3第一節 檢定群聚 3（一）總體性檢定(global test) 3（二）局部性檢定(local test) 4（三）焦點性檢定(focus test) 5第二節 近年相關研究回顧 5第三節 常見的群聚偵測方法 5（一）Choynowski Probability Mapping 6（二）Kulldorff & Nagarwalla Spatial Disease Clusters 6（三）Tango and Takahashi Flexible Scan Statistics 7第三章 兩階段的群聚檢測 9第一節 基本統計模型（群聚分析的常見假設） 9第二節 單一群聚兩階段群聚檢測方法 10第三節 多重群聚逐步檢測方法 11第四節方法特色與圖例說明 11第四章 群聚檢定方法與模擬檢定結果 13第一節 模擬設定與檢定表 13第二節 單一群聚檢測 15（一）顯著水準對型一誤差及檢定力的影響 15（二）疾病發生率及人口數對型一誤差及檢定力的影響 16（三）不同形狀位置群聚對檢定力的影響 18第三節 多重群聚檢測方法 21（一）兩群聚型一誤差 22（二）兩群聚檢定力比較 22第四節 單一群聚結果修正 26第五章 方法比較 30第一節 型一誤差 30第二節 單一中心圓形群聚檢定力比較 31第三節 單一中心長形群聚檢定力比較 33第四節 單一中心Y字形群聚檢定力比較 35第五節 模擬比較小結 38第六章 實證應用 39第一節 83年以及89年台灣癌症死亡人數 39第二節 台灣門診就診次數變化 43第三節 實證應用小結 44第七章 結論與後續研究 46第一節 結論 46第二節 後續研究： 47 | zh_TW |
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| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0933540041 | en_US |
| dc.subject (關鍵詞) | 空間群聚 | zh_TW |
| dc.subject (關鍵詞) | 累計型資料 | zh_TW |
| dc.subject (關鍵詞) | 疾病偵測 | zh_TW |
| dc.subject (關鍵詞) | 檢定力 | zh_TW |
| dc.subject (關鍵詞) | Spatial Cluster | en_US |
| dc.subject (關鍵詞) | Count Data | en_US |
| dc.subject (關鍵詞) | Disease Detection | en_US |
| dc.subject (關鍵詞) | Power | en_US |
| dc.title (題名) | 疾病群聚檢測方法與檢定力比較 | zh_TW |
| dc.title (題名) | Disease Cluster Detection Methods and Power Comparison | en_US |
| dc.type (資料類型) | thesis | en |
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