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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 廖振鐸 | |
dc.contributor.author | Chi-Rong Li | en |
dc.contributor.author | 李其融 | zh_TW |
dc.date.accessioned | 2021-06-13T00:20:10Z | - |
dc.date.available | 2007-08-01 | |
dc.date.copyright | 2007-07-27 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-07-25 | |
dc.identifier.citation | [1] Baker S. Identifying combinations of cancer markers for further study as triggers of early intervention. Biometrics 2000; 56: 1082-1087.
[2] Baker SG, Pinsky PF. A proposed design and analysis for comparing digital and analog mammography: Special receiver operating characteristic methods for cancer screening. Journal of the American Statistical Association 2001; 96: 421-428. [3] Bamber DC. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. Journal of Mathematical Psychology 1975; 12: 387-415. [4] Beam CA, Conant EF, Sickles EA, Weinstein SP. Evaluation of proscriptive health care policy implementation in screening mammography. Radiology 2003; 229: 534-540. [5] Beiden SV, Campbell G, Meier KL, Wagner RF. On the problem of ROC analysis with truth: the EM algorithm and the information matrix. Proceedings of SPIE 2000; 3981: 126-134. [6] Berger RL. Multivariate hypothesis testing and acceptance sampling. Technometrics 1982; 24: 295-300. [7] Box GEP, Cox DR. An analysis of transformations. Journal of the Royal Statistical Society: Series B 1964; 26: 211-252. [8] Choi YK, Johnson WO, Collins MT, Gardner IA. Bayesian inferences for receiver operating characteristic curves in the absence of a gold standard. Journal of Agricultural, Biological, and Environmental Statistics 2006; 11: 210-229. [9] DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 1988; 44: 837-845. [10] Dodd LE, Pepe MS. Partial AUC estimation and regression. Biometrics 2003; 59: 614-623. [11] Feinstein A. Principle of Medical Statistics. Chapman & Hall/CRC: Boca Raton, FL, USA, 2001; 455. [12] Gamage J, Mathew T, Weerahandi S. Generalized p-values and generalized con dence regions for the multivariate Behrens-Fisher problem and MANOVA. Journal of Multivariate Analysis 2004; 88: 177-189. [13] Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143: 29-36. [14] Hanning J, Iyer HK, Patterson P. Fiducial generalized con dence intervals. Journal of the American Statistical Association 2006; 101: 254-269. [15] Hsiao JK, Barko JJ, Potter WZ. Diagnosing diagnoses: receiver operating characteristic methods and psychiatry. Archives of General Psychiatry 1989; 46: 664-667. [16] Hsueh HM, Liu JP, Chen JJ. Unconditional exact tests for equivalence or noninferiority for paired binary endpoints. Biometrics 2001; 57: 478-483. [17] Jiang YL, Metz CE, Nishikawa RM. A receiver operating: Characteristic partial area index for highly sensitive diagnostic tests. Radiology 1996; 201: 745-750. [18] Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L. The use of receiver operating characteristic curves in biomedical informatics. Journal of Biomedical Informatics 2005; 38: 404-415. [19] Li CR, Liao CT, Liu JP. On the exact interval estimation for the difference in paired areas under the ROC curves. Statistics in Medicine; DOI: 10.1002/sim.2760 [20] Liao CT, Lin TY, Iyer HK. One- and two-sided tolerance intervals for general balanced mixed models and unbalanced one-way random models. Technometrics 2005; 47: 323-335. [21] Liao CT, Iyer HK. A tolerance interval for the normal distribution with several variance components. Statistica Sinica 2004; 14: 217-229. [22] Liu JP, Hsueh HM, Hsieh E, Chen JJ. Tests for equivalence or noninferiority for paired binary data. Statistics in Medicine 2002; 21: 231-245. [23] Liu JP, Ma MC, Wu CY, Tai JY. Tests of equivalence and noninferiority for diagnostic accuracy based on the paired areas under ROC curves. Statistics in Medicine 2006; 25: 1219-1238. [24] Liu A, Schisterman EF, Zhu Y. On linear combinations of biomarkers to improve diagnostic accuracy. Statistics in Medicine 2005; 24: 37-47. [25] Maitournan A, Simon R. On the e ciency of targeted clinical trials. Statistics in Medicine 2005; 24: 329-339. [26] Masaryk AM, Ross JS, DiCello MC, Modic MT, Paranandi L, Masaryk TJ. 3DFT MR angiography of the carotid bifurcation: Potential and limitations as a screening examination. Radiology 1991; 179: 797-804. [27] Mathew T, Webb DW. Generalized p values and con dence intervals for variance components: applications to army test and evaluation. Technometrics 2005; 47: 312-322. [28] McClish DK. Analyzing a portion of the ROC curve. Medical Decision Making 1989; 9: 190-195. [29] McNally RJ, Iyer HK, Mathew T. Tests for individual and population bioequivalence based on generalized p-values. Statistics in Medicine 2003; 22: 31-53. [30] McNeil BJ, Keller E, Adelstein SJ. Primer on certain elements of medical decision making. The New England Journal of Medicine 1975; 293: 211-215. [31] Metz CE. Basic principles of ROC analysis. Seminars in Nuclear Medicine 1978; 8: 283-298. [32] Metz CE. Some practical issues of experimental design and data analysis in radiological ROC studies. Investigation Radiology 1989; 24: 234-245. [33] Molodianovitch K, Faraggi D, Reiser B. Comparing the areas under two correlated ROC curves: parametric and non-parametric approaches. Biometrical Journal 2006; 48: 745 - 757. [34] Obuchowski NA. Testing for equivalence of diagnostic tests. American Journal of Roentgenology 1997; 168: 13-17. [35] Obuchowski NA. Receiver operating characteristic curves and their use in radiology. Radiology 2003; 229: 3-8. [36] Obuchowski NA, McClich DK. Sample size determination for diagnostic accuracy studies involving binormal ROC curve indices. Statistics in Medicine 1997; 16: 1529-1542. [37] Pepe MS. The Statistical Evaluation of Medical Tests for Classi cation and Prediction. Oxford University Press: New York, 2003. [38] Pepe MS, Longton G, Anderson GL, Schummer M. Selecting di erentially expressed genes from microarray experiments. Biometrics 2003; 59: 133-142. [39] Reiser B, Faraggi D. Con dence intervals for the generalized ROC criterion. Biometrics 1997; 53: 644-652. [40] Schuirmann DJ. A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of Bioavailability. Journal of Pharmacokinetics and Biopharmaceutics 1987; 15: 657-680. [41] Schisterman EF, Faraggi D, Reiser B. Adjusting the generalized ROC curve for covariates. Statistics in Medicine 2004; 23: 3319-3331. [42] Sen PK. On some convergence properties of U-Statistics. Calcutta Statistical Association Bulletin 1960; 10: 1-18. [43] Shapiro DE. The interpretation of diagnostic tests. Statistical Methods in Medical Research 1999; 8: 113-134. [44] Simon R, Maitournan A. Evaluating the e ciency of targeted designs for randomized clinical trials. Clinical Cancer Research 2004; 10: 6759-6763. [45] Su JQ, Liu JS. Linear combinations of multiple diagnostic markers. Journal of the American Statistical Association 1993; 88: 1350-1355. [46] Swain SM. A step in the right direction. Journal of Clinical Oncology 2006; 24: 3717-3718 [47] Tang NS, Tang ML, Chan ISF. On tests of equivalence via non-unity relative risk for matched-pair design. Statistics in Medicine 2002; 22: 1217-1233. [48] Tang S, Tsui KW. Distributional properties for the generalized p-value for the Behrens-Fisher problem. Statistics and Probability Letters 2007; 77: 1-8. [49] The US FDA Draft Guidance on In Vitro Diagnostic Multivariate Index Assays. FDA: Rockville, MD, 2006. [50] Thompson ML, Zucchini W. On the statistical analysis of ROC curves. Statistics in Medicine 1989; 8: 1277-1290. [51] Tsui KW, Weerahandi S. Generalized p-values in signi cance testing of hypotheses in the presence of nuisance parameters. Journal of the American Statistical Association 1989; 84: 602-607. [52] Weerahandi S. Testing regression equality with unequal variances. Econometrica 1987; 55: 1211-1215. [53] Weerahandi S. Generalized con dence intervals. Journal of the American Statistical Association 1993; 88: 899-905. [54] Weerahandi S. Exact Statistical Methods for Data Analysis. Springer: New York, 1995. [55] Weerahandi S. Generalized Inferences in Repeated Measures: Exact Methods in MANOVA and Mixed Models. Wiley: New York, 2004. [56] Welch BL. The signi cance of the di erence between two means when the population variances are unequal. Biometrika 1937; 29: 350-360. [57] Welch BL. The generalization of 'Student's' problem when several different population variances are involved. Biometrika 1947; 34: 28-35. [58] Wieand S, Gail MH, James BR, James KL. A family of nonparametric statistics for comparing diagnostic markers with paired or unpaired data. Biometrika 1989; 76: 585-592. [59] Wolski WE, Lalowski M, Martus P, Herwig R, Giavalisco P, Gobom J, Sickmann A, Lehrach H, Reinert K. Transformation and other factors of the peptide mass spectrometry pairwise peak-list comparison process BMC Bioinformatics 2005; 6: 285. [60] Yao Y. An approximate degrees of freedom solution to the multivariate Behrens-Fisher problem. Biometrika 1965; 52: 139-147. [61] Zhou XH, Obuchowski NA, McClish DK. Statistical Methods in Diagnostic Medicine. Wiley: New York, 2002. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28737 | - |
dc.description.abstract | 接收器操作特徵曲線(receiver operating characteristic curve)簡稱ROC曲線,是目前廣為用來評估診斷工具準確性的統計方法。它已經成功地應用在放射學(radiology)、精神病學(psychiatry)、流行病學(epidemiology)、生物資訊學(biomedical informatics)等領域。一般常用來總結ROC曲線訊息的指標為ROC曲線下面積與ROC曲線下部分面積。ROC曲線下面積為特異度(specificity)全域內的平均可靠度(sensitivity);ROC曲線下部分面積則是特異度被限制在臨床上有意義的範圍內之平均可靠度。在診斷試驗中,比較新診斷工具與現行標準診斷工具的準確性(accuracy)是一項重要的課題。利用廣義p值(generalized p-value)與廣義信賴區間(generalized confidence interval)的概念,本論文針對成對(paired)ROC曲線下面積和部分面積提出確切的統計推論(exact inferences)。此外,我們亦延伸我們提出的方法來比較兩個擁有多個變數(multiple markers)診斷工具的準確性。透過大規模的統計模擬研究可驗證我們提出的確切檢定能控制型一誤差機率接近宣稱的水準(nominal level);我們提出的確切區間估計亦能提供足夠的覆蓋機率(coverage probability)。一般而論,我們提出的確切方法優於最大概似估計法(maximum likelihood estimate)以及無母數估計法(nonparametric estimate)。最後,利用我們提出的方法針對幾組胰臟癌(pancreatic cancer)、動脈硬化症(atherosclerosis)、卵巢癌(ovarian cancer)等實際診斷資料進行分析。 | zh_TW |
dc.description.abstract | The receiver operating characteristic (ROC) curve is currently a popular statistical tool for the accuracy of diagnostic device. It has been widely used in various practical applications, such as radiology, psychiatry, epidemiology, biomedical informatics, etc. One of the primary objectives of diagnostic trials is to compare the diagnostic accuracy of the new diagnostic device to that of the current standard device. The area under the ROC curve (AUROC) is a summary index that is interpreted as the average of true positive rate over entire false positive rates. The partial area under the ROC curve (PAUROC) is another summary index that restricts attention to a specified range of clinical interest. They can be usually used as the bases of inferential statistics for comparing ROC curves. In this dissertation, we develop exact inferences for comparing paired AUROCs and paired PAUROCs based on the concept of generalized p-values and generalized confidence intervals. In addition, we extend the results to compare the paired ROC curves which are constructed by multiple markers. Simulation results demonstrate that the exact test based on generalized p-values adequately controls the size at the nominal level; the exact interval estimation based on the generalized confidence intervals provides not only sufficient coverage probability but also reasonable expected length. In general, the proposed methods outperform some published asymptotic maximum likelihood methods and nonparametric methods in various simulation scenarios. Furthermore, numerical examples using published datasets illustrate the proposed methods. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T00:20:10Z (GMT). No. of bitstreams: 1 ntu-96-D91621201-1.pdf: 773734 bytes, checksum: 1bce245d499e13fb887f149988d68840 (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | 1 Introduction ......1
1.1 The paired areas under the ROC curves ......2 1.2 The paired partial areas under the ROC curves ......3 1.3 The paired areas under the ROC curves with multiple markers ...6 1.4 Summary ......7 2 Exact Inferences ......9 2.1 Genealized p-values ......10 2.2 Generalized condence intervals ......11 3 An Exact Interval Estimation on Paired Areas under the ROC curves ......12 3.1 Nonparametric and Maximum Likelihood Methods ......13 3.2 Exact interval estimation ......16 3.3 Simulation Studies ......20 3.4 Numerical Examples ......36 3.4.1 The study of pancreatic cancer serum biomarkers ......36 3.4.2 The study of accuracy of MRA readings for two readers ......37 3.5 Discussion ......38 4 An Exact Non-inferiority Test on Paied Partial Areas under the ROC curves ......40 4.1 Non-inferiority Hypotheses and the Methods of Estimation ......41 4.2 Exact Test Using Generalized p-Values ......44 4.3 Simulation Studies ......47 4.4 Numerical Example ......54 4.5 Discussion ......56 5 An Exact Interval Estimation on Paired Areas under the ROC curves with Multiple Markers ......60 5.1 Comparing diagnostic accuracy with multiple markers ......61 5.2 Generalized condence intervals ......63 5.3 Simulation studies ......66 5.4 Numerical example ......70 5.5 Discussion ......71 6 Future Research ......74 Bibliography ......77 Appendix A ......85 Appendix B ......88 Appendix C ......92 | |
dc.language.iso | en | |
dc.title | 成對ROC曲線之確切推論 | zh_TW |
dc.title | Exact Inferences on Paired ROC Curves | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 陳珍信,蕭朱杏,江金倉,劉仁沛 | |
dc.subject.keyword | 廣義檢定變數, 廣義樞紐量, 廣義p值, 廣義信賴區間, 多重標記, | zh_TW |
dc.subject.keyword | generalized test variable, generalized pivotal quantity, generalized p-value, generalized confidence interval, multiple markers, | en |
dc.relation.page | 84 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2007-07-27 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 農藝學研究所 | zh_TW |
顯示於系所單位: | 農藝學系 |
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