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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡政安(Chen-An Tsai),劉仁沛(Jen-Pei Liu) | |
| dc.contributor.author | Kuan-Ting Lee | en |
| dc.contributor.author | 李冠霆 | zh_TW |
| dc.date.accessioned | 2021-06-15T16:16:53Z | - |
| dc.date.available | 2016-08-20 | |
| dc.date.copyright | 2015-08-20 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-08-17 | |
| dc.identifier.citation | REFERENCES
中文文獻: 陳景祥 (2010) R軟體:應用統計方法 第一版, 東華書局股份有限公司 英文文獻: Basford K.E., Greenway D.R., McLachlan G.J., Peel D. (1997). Standard errors of fitted component means of normal mixtures, Computational Statistics. 12(1):1-17. Buyse M., Loi S., van’t Veer L., et al. (2006). Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer, Journal of the National Cancer Institute, 98, 1183-1192. Breslow N. (1974). Covariance analysis of censored survival data. Biometrics, 30: 89–99. Cox D.R. (1972). Regression models with life tables, Journal of the Royal Statistical Society. Series B. 34: 187-220 Dempster A.P., Laird N.M. and Rubin D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society. Series B (Methodological). 39(1): 1-38 Efron B. and Tibshirani R.J. (1993). An Introduction to the Bootstrap, Chapman and Hall, New York, USA. Eng K.H. and Hanlon B.M. (2014). Discrete mixture modeling to address genetic heterogeneity in time-to-event regression, Bioinformatics. 30:1690-1697 Chow S.C. and Liu J.P. (2012). Design and Analysis of Clinical Trials, 3nd Ed., John Wiley and Sons, New York, USA. Liu J.P. and Chow S.C. (2008). Issues on the diagnostic multivariate index assay and targeted clinical trials, Journal of Biopharmaceutical Statistics. 18: 167-182. Liu J.P. and Lin J.R. (2008). Statistical methods for targeted clinical trials under enrichment design. Journal of the Formosan Medical Association.107: S35-S42. Liu J.P., Lin J.R. and Chow S.C. (2009). Inference on treatment effects for targeted clinical trials under enrichment design. Pharmaceutical Statistics. 8: 356-370 Maitournam A. and Simon R. (2005). On the efficiency of targeted clinical trials. Statistics in Medicine. 24: 329-339. McLachlan G.J. and Krishnan T. (1997). The EM algorithm and Extensions, Wiley, New York Simon R. (2008). The use of genomics in clinical trial design. Clinical Cancer Research. 14: 5984-93. Simon R. and Maitournam A. (2004). Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical Cancer Research. 10: 6759- 6763. Ravdin P.M. and Chamness G.C. (1995). The c-erbB-2 proto-oncogene as a prognostic and predictive markers in breast cancer: A paradigm for the development of other macromolecular markers-a review. Gene, 159, 19-27. Seshadri R., Figaira E.A., Horsfall D.J., McCaul K., Setlur V., and Kitchen P. (1993). Clinical significance of HER-2/neu oncogene amplification in primary breast cancer. The South Australian Breast Cancer Study Group. Journal of Clinical Oncology. 11: 1936-1942. Slamon D.J., Leyland-Jones B., Shak, S., et al. (2001). Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2, New England Journal of Medicine. 344: 783-792. U.S. FDA (2005). The draft concept paper on Drug-Diagnostic Co-Development. The U.S. Food and Drug Administration, Rockville, Maryland, USA. U.S. FDA (2006). Annotated Redlined Draft Package Insert for Herceptin. The U.S. Food and Drug Administration, Rockville, Maryland, U.S.A. U.S. FDA (2007a). Decision Summary k062694. Rockville, Maryland, U.S.A. The U.S. FDA (2011). Draft Guidance on In Vitro Companion Diagnostic Devices. The U.S. Food and Drug Administration, Rockville, Maryland, USA. The U.S. FDA (2011). Draft Guidance on In Vitro Companion Diagnostic Devices. The U.S. Food and Drug Administration, Rockville, Maryland, USA. Wu C.F. (1983). On the convergence properties of the EM algorithm. The Annals of Statistics. 11: 95-103. US National Cancer Institute “Advances in Targeted Therapies Future” http://www.cancer.gov/about-cancer/treatment/types/targeted-therapies/targeted-therapies-fact-sheet. Accessed date: 2015/4/20 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52508 | - |
| dc.description.abstract | 在傳統的臨床實驗中，納入以及排除是基於臨床指標所考量的，但往往未考慮到受試者的基因或是基因體的變異。在人類基因體計畫完成後，許多疾病的分子標的可以被鑑別，因此可以發展出分子標的治療方法，但鑑定分子標的之診斷試劑通常並非完全準確，所以有些診斷標的臨床實驗的陽性病人實際上可能並沒有分子標的，因此對於真正擁有分子標的之病人族群而言，標的臨床實驗下之標的療法的療效估計值會有偏差。因此，我們提出對於真正擁有分子標的之病人配合標的治療之不偏推論統計方法。在強化設計的臨床試驗及半參數考克斯比例風險模型下，我們針對設限資料來探討處理效應並同時可考慮許多共變數來鑑定分子標的之診斷試劑的準確度，我們採用Eng K.H.及Hanlon B.M. (2014) 所提出之混合考克斯比例風險模型，並加以應用，且藉由EM演算法推導出考克斯比例風險模型下風險比例的估計式並且利用拔靴法來計算估計值之變異數。運用模擬研究來驗證所得之估計值與檢定程序而加以比較與現有方法之間的差異，及以實例數據以說明方法的應用。 | zh_TW |
| dc.description.abstract | In traditional clinical trials, inclusion and exclusion criteria are considered based on some clinical endpoints, the genetic or genomic variability of the trial participants are not totally utilized in the criteria. After the Human Genome Project is completed, many molecules underlying disease can be identified, it is possible to develop a targeted molecular therapy. However, the accuracy of diagnostic devices for identification of such molecular targets is usually not perfect. Some patients with positive diagnosis result is actually might not have the specific molecular targets. As a result, the treatment effect may be underestimated in the patient population truly with the molecular target. In order to resolve this issue, we propose a method based on the mixture Cox’s proportional model for the k latent classes (Eng K.H. and Hanlon B.M., 2014) and under the enrichment design. We develop inferential procedures for the treatment effects of the targeted drug based on the censored endpoints in the patients truly with the molecular targets which also incorporates the inaccuracy of the diagnostic device for detection of the molecular targets on the inference of the treatment effects. We propose using the EM algorithm in conjunction with the bootstrap technique for estimation of hazard ratio and its variance. Though the simulation study, we empirically investigate the performance of the proposed methods and to compare with the current method. The numerical examples illustrate the proposed procedures. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T16:16:53Z (GMT). No. of bitstreams: 1 ntu-104-R02621207-1.pdf: 1558768 bytes, checksum: 93d5e95c83cd43e149b4f5cc3a9f4e91 (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | CONTENTS
口試委員會審定書 # 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 1.1 Accuracy of Diagnostic Devices 4 1.2 Statistical Designs 8 1.3 Aims 10 Chapter 2 Literature Review 17 2.1 Efficiency of Enrichment Design 18 2.2 EM Algorithm for Cox PH Mixture model 19 2.3 Convergence of EM Algorithm 21 2.4 Estimator of the Standard Error 21 Chapter 3 Statistical Inference under the Semiparametric Proportional Hazard Regression Model 24 3.1 Current Methods 24 3.2 The Proposed Procedure 28 3.3 Numerical Example 36 Chapter 4 Simulation Studies 40 4.1 Simulation Procedure 40 4.2 Simulation Results 43 Chapter 5 Discussion 63 REFERENCES 65 Appendix R Codes for Simulation 68 LIST OF FIGURES Figure 1.1 Unselected design for targeted clinical trials 14 Figure 1.2 Stratified design for targeted clinical trials 15 Figure 1.3 Enrichment design for targeted clinical trials 16 Figure 4.1 Flow chart of the simulation study to semiparametric Cox’s proportional hazard regression model 45 Figure 4.2 The relative bias curve between EM approach and Current approach for N=300 49 Figure 4.3 The relative bias curve between EM approach and Current approach for N=600 49 Figure 4.4 The relative bias curve between EM approach and Current approach for N=900 49 Figure 4.5 The empirical power curve when the PPV is 0.6, N=300 and CR=10%......................................................................................................49 Figure 4.6 The empirical power curve between EM approach and Current approach for N=300 50 Figure 4.7 The empirical power curve between EM approach and Current approach for N=600 51 Figure 4.8 The empirical power curve between EM approach and Current approach for N=900 52 LIST OF TABLES Table 1.1 Phase III clinical efficacy in the first-line treatment 12 Table 1.2 Treatment effect versus level of HER2 expression phase III randomized trial 13 Table 3.1 Population mean survival time by treatment and diagnosis 36 Table 3.2 Treatment effects as a function of a specific biomarker overexpression. 37 Table 3.3 Point and interval estimator of hazard ratio for mortality 38 Table 4.1 Relative bias (%) and the coverage probability under the semiparametric Cox’s proportional hazard model for N=300 53 Table 4.2 Relative bias (%) and the coverage probability under the semiparametric Cox’s proportional hazard model for N=600 55 Table 4.3 Relative bias (%) and the coverage probability under the semiparametric Cox’s proportional hazard model for N=900 57 Table 4.4 Comparison of empirical sizes under the semiparametric Cox’s proportional hazard model 59 Table 4.5 Comparison of empirical powers under the semiparametric Cox’s proportional hazard model for N=300 60 Table 4.6 Comparison of empirical powers under the semiparametric Cox’s proportional hazard model for N=600 61 Table 4.7 Comparison of empirical powers under the semiparametric Cox’s proportional hazard model for N=900 62 | |
| dc.language.iso | en | |
| dc.subject | 設限資料 | zh_TW |
| dc.subject | 標的臨床實驗 | zh_TW |
| dc.subject | 考克斯比例風險模型 | zh_TW |
| dc.subject | 強化設計 | zh_TW |
| dc.subject | EM演算法 | zh_TW |
| dc.subject | censored data | en |
| dc.subject | Targeted clinical trials | en |
| dc.subject | EM algorithm | en |
| dc.subject | Cox proportion hazard model | en |
| dc.subject | Enrichment design | en |
| dc.title | 在強化設計及考克斯比例風險模型下設限資料評估標靶藥物統計分析方法之研究 | zh_TW |
| dc.title | Statistical analysis of censored endpoints under the Cox proportional hazard model for evaluation of targeted drug products under the enrichment design | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 季瑋珠(Wei-Chu Chie),林志榮(Jr-Rung Lin) | |
| dc.subject.keyword | 標的臨床實驗,強化設計,設限資料,EM演算法,考克斯比例風險模型, | zh_TW |
| dc.subject.keyword | Targeted clinical trials,Enrichment design,censored data,EM algorithm,Cox proportion hazard model, | en |
| dc.relation.page | 83 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-08-17 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 農藝學研究所生物統計組 | zh_TW |
| 顯示於系所單位： | 農藝學系 | |
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