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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.

Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第17261号 / 工博第3663号 / 新制||工||1557(附属図書館) / 30018 / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 椎葉 充晴, 教授 寶 馨, 准教授 立川 康人 / 学位規則第4条第1項該当

假設二元反應曲線為簡單線性羅吉斯迴歸模型(Simple Linear Logistic Regression Model)，在樣本數為偶數的前題下，所謂的最佳D型設計(D-Optimal Design)是直接將半數的樣本點配置在第17.6個百分位數，而另一半則配置在第82.4個百分位數。很遺憾的是，這兩個位置在參數未知的情況下是無法決定的，因此逐次實驗設計法(Sequential Experimental Designs)在應用上就有其必要性。在大樣本的情況下，本文所探討的逐次實驗設計法在理論上具有良好的漸近最佳D型性質(Asymptotic D-Optimality)。尤其重要的是，這些特性並不會因為起始階段的配置不盡理想而消失，影響的只是收斂的快慢而已。但是在實際應用上，這些大樣本的理想性質卻不是我們關注的焦點。實驗步驟收斂速度的快慢，在小樣本的考慮下有決定性的重要性。基於這樣的考量，本文將提出三種起始階段設計的方法並透過模擬比較它們之間的優劣性。 / The D-optimal design is well known to be a two-point design for the simple linear logistic regression function model. Specif-ically , one half of the design points are allocated at the 17.6- th percentile, and the other half at the 82.4-th percentile. Since the locations of the two design points depend on the unknown parameters, the actual 2-locations can not be obtained. In order to dilemma, a sequential design is somehow necessary in practice. Sequential designs disscused in this context have some good properties that would not disappear even the initial stgae is not good enough under large sample size. The speed of converges of the sequential designs is influenced by the initial stage imposed under small sample size. Based on this, three initial stages will be provided in this study and will be compared through simulation conducted by C++ language.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23167号 / 工博第4811号 / 新制||工||1752(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 岸田 潔, 教授 三村 衛, 准教授 PIPATPONGSA Thirapong / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM