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研究生:施文琪
研究生(外文):Wen-xi Shi
論文名稱:評估檢定多個診斷方法下AUROC相似性及非劣性統計方法之研究
論文名稱(外文):Evaluation of Statistical Methods for Equivalence and Non-inferiority Based on the Multiple Correlated AUROCs
指導教授:馬瀰嘉馬瀰嘉引用關係
指導教授(外文):Mi-chia Ma
學位類別:碩士
校院名稱:國立成功大學
系所名稱:統計學系碩博士班
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:80
中文關鍵詞:模擬ROC曲線相似性診斷方法非劣性
外文關鍵詞:diagnostic markerROC curvesimulationnon-inferiorityequivalence
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在臨床上,由於新的診斷方法不斷地推陳出新,ROC曲線下的面積被廣泛地應用在各種領域中,作為測量診斷方法精確度的工具。近年來,對於測試療法和傳統療法之間的比較,相似性及非劣性檢定成為一個重要的比較方法;而且愈來愈多的文獻報告針對相似性及非劣性檢定方法提出相關的研究。此篇論文主要是將無母數法、標準化差評估法以及該兩種方法所對應的拔靴法這四種方法推廣應用到多個診斷群,去同時評估多個診斷群相似性及非劣性檢定方法,並以模擬結果比較這四種方法在雙常態分配假設下各種參數組合的優劣情形。
In clinical study, since the new diagnosis test needs through the old to unceasingly bring forth the new, the area under ROC curve is widely applied in many kinds of field and is used as a tool to decide the accuracy of diagnosis tests. In recent years, testing the equivalence or non-inferiority of treatments becomes an important assessment method. Moreover, many literatures increasingly focused on the equivalence or non-inferiority test. In this paper, the four proposed methods including the nonparametric method, the method based on the standardized difference and their corresponding bootstrap method are applied to the equivalence and non-inferiority tests for multiple diagnostic markers. Then, a simulation study under binormal distribution is conducted to compare the empirical size and empirical power of these four methods for various combinations of parameters.
Chapter 1 Introduction………………………………………………1
1.1 The ROC Curve………………………………………………3
1.2 The Area Under ROC Curve (AUROC)……………………4
1.3 Tests for Equivalence or Non-inferiority…………6
Chapter 2 Literature Review for Equivalence/
Non-inferiority Test……...9
2.1 Nonparametric Method……………….…………………………9
(I) Interval Hypotheses for Two AUROCs…………………9
(II) Asymptotic Tests………………………………………10
(III) Estimation of Areas Under Correlated ROC
Curves………..11
2.2 Method Based on the Standardized Difference…………14
2.2.1 Estimation of Areas Under Two Correlated ROC
Curves…...14
2.2.2 Hypotheses For Equivalence and Non-inferiority
Test……...18
Chapter 3 Proposed Methods……………………………………20
3.1 The Nonparametric Method………………………………20
3.2 The Method Based on the Standardized Difference……21
3.3 The Bootstrap Method………………………………………24
Chapter 4 Simulations and Results….………………………28
4.1 Simulation Process and Parameter Combinations………28
4.2 Simulation Results…………………………………………31
Chapter 5 Numerical Example………………………………………36
Chapter 6 Conclusion………………………………………………47
Reference…………………………………………………………...48
Appendix A…………………………………………………………50
[1]DeLong, E., DeLong, D., Clarke-Pearson, D.(1988). Comparing the areas under two or more correlated receiver operation characteristic curves: A nonparametric
approach. Biometrics, Vol.44, 837– 845.
[2]Efron, B. and Tibshirani, R.J. (1986). Bootstrap methods for standard errors,confidence intervals, and other measures of statistical accuracy. Statistical Science, Vol.1, 54 –77.
[3]Efron, B. and Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman and Hall, United States of America, 168–176.
[4]Griner et al. (1981). Selection and interpretation of diagnostic test procedures, principles and applications. Annals of Internal Medicine, Vol.94, 553-600.
[5]Hanley, J.A. and McNeil, B.J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC). Radiology, Vol.143, 29-36.
[6]Hoeffding, W. (1948). A class of statistics with asymptotically normal distribution. Annals of Mathematical Statistics, Vol.19, 293-325.
[7]Hsueh, H. M., Liu, J. P. and Chen, J. J. (2001). Unconditional exacts tests for equivalence or non-inferiority for paired binary endpoints. Biometrics, Vol.57, 478-483.
[8]Liu, J.P., Hsueh, H.M., Hsieh, E., and Chen, J.J. ( 2002). Tests for equivalence or non-inferiority for paired binary data. Statistics in Medicine, Vol.21, 231–245.
[9]Liu, J.P., Ma, M.C.,Wu, C.Y., and Tai, J.Y. (2006) Tests for equivalence and non-inferiority for diagnostic accuracy based on the paired areas under ROC
curves. Statistics in Medicine, Vol. 25, 1219-1238.
[10] Lu, Y. and Bean, J.A. (1995). On the sample size for one-sided equivalence of sensitivities based upon McNemar’s test. Statistics in Medicine, Vol.14, 1831-1839.
[11] Luchenbruch, P. A. and Lynch, C. J. (1998). Assessing screening tests:extensions of McNemar’s test. Statistics in Medicine, Vol.17, 2207-2217.
[12] Metz, C. E. (1978). Basic principles of ROC analysis. Seminars in Nuclear Medicine, Vol.8, 283-298.
[13]Metz, C. E. (1989). Some practical issues of experimental design and data analysis in radiological ROC studies. Investigative Radiology, Vol.24, 234-245.
[14] Nam, J-m. (1997). Establishing equivalence of two treatments and sample size requirements in matched-pairs design. Biometrics, Vol.17, 1422-1430.
[15] Obuchowski, N.A., and McClish, D.K. (1997). Sample size determination for diagnostic accuracy studies involving binormal ROC curve indices. Statistic in
medicine, Vol.16, 1529-1542.
[16] Obuchowski N. (2001). Can electronic images replace hard-copy film? Defining and testing the equivalence of diagnostic tests. Statistics in Medicine, Vol.20, 2845 –2863.
[17] Obuchowski, N.A. (2003). Receiver operating characteristic curves and their use in radiology. Radiology, Vol.229, 3–8.
[18] Parker, C.B. and Delong, E.R. (2003). ROC methodology within a monitoring framework. Statistics in Medicine, Vol.22, 3473–3488.
[19] Zweig, M.H. and Campbell, G.. (1993). Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry, Vol.39, 561-577.
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