系統識別號 U0026-0812200910384002 論文名稱(中文) 多變量ROC曲線下面積估計量之研究 論文名稱(英文) The estimation of the area under multivariate ROC curve 校院名稱 成功大學 系所名稱(中) 統計學系碩博士班 系所名稱(英) Department of Statistics 學年度 91 學期 2 出版年 92 研究生(中文) 歐茹琪 研究生(英文) Ju-Chi Ou 電子信箱 olayozh@yahoo.com.tw 學號 r2690108 學位類別 碩士 語文別 英文 論文頁數 162頁 口試委員 口試委員-劉仁沛口試委員-陳俞成指導教授-馬瀰嘉 中文關鍵字 無母數估計量  模型導向估計量  多變量ROC曲線 英文關鍵字 nonparametric estimation  multivariate ROC curve  model-based esitmation 學科別分類 中文摘要 關於兩種或多種診斷方法,在診斷的預測上是否有顯著差異的檢定，在過去的文獻中已有一些研究，且對於一種診斷的正確性，通常利用ROC曲線下面積來評估。但在目前的診斷方法中，利用多個診斷方法同時評估某種疾病是常見的，例如糖尿病，但尚未有一個適當測度量可以評斷這些診斷方法的準確性。本論文主要提出一個整合的測度量來評估多種診斷方法同時使用下的準確性，在同時考慮多個診斷方法下，利用隨機效果模式架構了多變量ROC曲線下面積求法，及探討其性質。最後，將所導出的方法應用在一組糖尿病的資料上。 英文摘要 A statistical tool which is popular for describing the accuracy of diagnostic is the receive operating characteristic (ROC) curve. There has been an increased use of ROC curve for assessing the effectiveness of continuous diagnostic markers in distinguishing between diseased and healthy individuals. McClish (1989) recognized that the area under the ROC curve(AUROC) is a global measure of a test's accuracy because it includes the entire range of false-positive rates from 0.0 to 1.0. In fact, it is possible that several diagnostic markers are used to detect a disease , e.g. diabetes, and how one can measure their simultaneous diagnostic accuracy. Based on the random effect model, we derive an estimate of the area under multivariate ROC curve. A model-based estimation and an approximated confidence interval are derived by combining the confidence intervals of two parameters using Bonferroni method. A nonparametric estimation will also be proposed and compared to the model-based estimate by a simulation study. Finally, this method is applied to a real data for example. 論文目次 1.Introduction....................................1 1.1 The Area under the ROC Curve..................2 2. The Area under Multivariate ROC Curve (AUMROC)...7 3. The Statistical Properties of AUMROC.............13 4. Point and Interval Estimation of θ..............15 4.1 Model-based estimation........................15 4.2 Nonparametric estimation......................22 4.3 The bootstrap method..........................24 5. Example..........................................25 6. Simulation and Results...........................29 7.Discussion........................................32 參考文獻 1.Cochran, W.G., Testing a linear relation among variances, Biometrics, 7, 17-32, 1951. 2.Dawson-Saunders, B., & Trapp, R. G., Basic and clinical biostatistics., Norwalk, CT : Appleton & Lange, 1990. 3. Efron, B. and Tibshirani, R. J., it An Introduction to the Bootstrap,Chapman and Hall, United States of America, 1993. 4.Emir, B. Wieand, S. Jung, S. H. and Ying, A.,Comparison of diagnostic markers with reported measurements : a non-parametric ROC curve approach, Statistics in Medicine, Vol.19, 511-523, 2000. 5. 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M., &, Monahan, J., Psychological Science can improve diagnostic decisions, Psychological Science in the Public Interest, Vol.1, 1-26, 2000. 21.Swets, J. A., Measuring the accuracy of diagnostic systems, Science, 240, 1285-1293, 1988. 22.Swets, J. A, and Pickett, R. M., Evaluation of diagnostic systems: Methods from signal detection theory, Academic Press, New York, 1982. 論文全文使用權限 同意授權校內瀏覽/列印電子全文服務，於2004-07-11起公開。同意授權校外瀏覽/列印電子全文服務，於2004-07-11起公開。

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