跳到主要內容

臺灣博碩士論文加值系統

(18.205.192.201) 您好!臺灣時間:2021/08/05 10:40
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:林志榮
研究生(外文):Jr-Rung Lin
論文名稱:強化設計標的臨床試驗下處理效應之統計推論
論文名稱(外文):Statistical Inference on Treatment Effects for Targeted Clinical Trials under Enrichment Design
指導教授:劉仁沛劉仁沛引用關係
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:農藝學研究所
學門:農業科學學門
學類:一般農業學類
論文種類:學術論文
論文出版年:2008
畢業學年度:97
語文別:英文
論文頁數:121
中文關鍵詞:標的臨床試驗強化設計EM演算法拔靴法貝氏估計陽性預測值
外文關鍵詞:Targeted clinical trialsEnrichment designEM algorithmBootstrap methodBayesian approachPositive predictive value
相關次數:
  • 被引用被引用:0
  • 點閱點閱:173
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
於人類基因體計畫完成後,疾病的分子標的可被鑑別,因此可以發展出分子標的形式的治療方法。在實務上,標的臨床試驗通常是用來評估個別化臨床處置的可能性及可行性。但是鑑定分子標的之診斷試劑通常並非完全準確,所以有些納入標的臨床試驗的陽性診斷病人實際上可能並沒有此分子標的,因此對於真正擁有分子標的之病人族群而言,標的臨床試驗下之標的療法的療效估計值會有偏差。 因此對於真正擁有分子標的之病人,我們則提出標的療法之無偏推論的統計方法。在強化設計的臨床試驗下,考慮鑑定分子標的之診斷試劑的準確度,我們提出利用EM演算法配合拔靴技術與貝氏方法來做處理效應之推論。運用模擬研究以驗證所得之估計值與檢定程序,並提出實例數據以說明方法的應用。對於推論真正擁有分子標的之病人族群的療效,我們所提出的之估計值及檢定程序為適當的統計方法。
After completion of the Human Genome Project (HGP), the disease targets at molecular levels can be identified. As a result, treatment modality for molecular targets can be developed. In practice, targeted clinical trials are usually conducted for evaluation of the possibility and feasibility of the individualized treatment of patients. However, the accuracy of diagnostic devices for identification of such molecular targets is usually not perfect. Therefore, some of the patients enrolled in targeted clinical trials with a positive result for molecular target might not have the specific molecular targets and hence the treatment effects of the targeted therapy estimated from targeted clinical trials could be biased for the patient population truly with the molecular targets. We develop statistical methods for an unbiased inference for the targeted therapy in the patients truly with the molecular targets. Under the enrichment design, we propose using the EM algorithm in conjunction with the bootstrap technique and the Bayesian method to incorporate the inaccuracy of the diagnostic device for detection of the molecular targets on the inference of the treatment effects. The simulation studies were conducted to empirically investigate the performance of the proposed estimations and testing procedures. Numerical example illustrates the application of the proposed method. Our proposed estimations and testing procedures are adequate statistical methods for the inference of the treatment effects for the patients truly with molecular targets.
CHAPERT 1 INTRODUCTION 1
1.1 ACCURACY OF DIAGNOSTIC DEVICES 3
1.2 STATISTICAL DESIGNS 8
1.3 SUMMARY 13
CHAPERT 2 LITERATURE REVIEW 15
2.1 STATISTICAL METHODS UNDER ENRICHMENT DESIGN 16
2.2 EM ALGORITHM 17
2.3 CONVERGENCE OF EM ALGORITHM 18
2.4 ESTIMATOR OF THE STANDARD ERROR 19
2.5 MARKOV CHAIN MONTE CARLO (MCMC) 20
2.6 CONJUGATE PRIORS 22
2.7 GIBBS SAMPLER 25
CHAPERT 3 STATISTICAL INFERENCE TO CONTINUOUS ENDPOINTS 27
3.1 CURRENT METHODS 27
3.2 THE PROPOSED PROCEDURE 30
3.3 SAMPLE SIZE CALCULATION 37
CHAPERT 4 STATISTICAL INFERENCE TO BINARY ENDPOINTS 39
4.1 CURRENT METHODS 39
4.2 THE PROPOSED PROCEDURE 42
CHAPERT 5 SIMULATION STUDIES 47
5.1 CONTINUOUS ENDPOINTS 47
5.1.1 Simulation Procedure 47
5.1.2 Simulation Results 49
5.2 BINARY ENDPOINTS 53
5.2.1 Simulation Procedure 53
5.2.2 Simulation Results 54
CHAPERT 6 NUMERIC EXAMPLES 77
6.1 CONTINUOUS ENDPOINTS 77
6.2 BINARY ENDPOINTS 79
CHAPERT 7 DISCUSSION 85
REFERENCES 99
APPENDIX A 103
Basford K.E., Greenway D.R., McLachlan G.J. and Peel D. (1997). Standard Errors of Fitted Component Means of Normal Mixtures, Computational Statistics. 12(1):1-17.
Best N. G., Cowles M. K. and Vines S. K. (1997). CODA: Convergence diagnosis and output analysis software for Gibbs sampling output, Version 0.4. MRC Biostatistics Unit, Cambridge:http://www.mrc-bsu.cam.ac.uk/bugs/classic/coda04/readme.shtml
Carlin, B.P., and Louis, T.A. (1998). Bayes and Empirical Bayes for Data Analysis, Chapman and Hall, New York.
Casciano DA, Woodcock J. (2006). Empowering microarrays in the regulatory setting, Nature Biotechnology. 24: 1103.
Chow S.C. and Liu J.P. (2004). Design and Analysis of Clinical Trials 2nd Ed., John Wiley and Sons, 3rd Ed., New York, USA.
Dalton WS, Friend SH. (2006). Cancer Biomarkers–an invitation to the table, Science. 312: 1165-8.
Dempster AP, Laird NM, Rubin DB. (1997). Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion), J. Roy. Statist. Soc. B. 39: 1–38.
Efron B. and Tibshirani R.J. (1993). An Introduction to the Bootstrap, Chapman and Hall: New York.
Fleiss J.L., Levin B. and Paik M.C. (2003). Statistical Methods for Rates and Proportions, 3rd Ed. John Wiley and Sons, New York.
Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-6: 721-741.
Hastings, W.K. (1970). Monte Carlo sampling methods using Markov chains and their application. Biometrika. 57: 97-109.
Kathiresan, S., Melander, O., Anevski, D., et al. (2008). Polymorphism associated with cholesterol and risk of cardiovascular events, New England Journal of Medicine. 358: 1240-49.
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.
Maitournam A., 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
McLachlan G.J. and Peel D. (2000). Finite Mixture Models, Wiley, New York.
Metropolis, N., Rosenbluth, A., Rosenbluth, R., Teller, A. and Teller, E. (1953). Equation of state calculations by fast computing machines. J. Chem. Phys. 21: 1087-1092
Miller, N.E., and Miller, G.J. (1975). High-density lipoprotein and atherosclerosis, Lancet. 1: 1033.
Namboodiri, K.K., Kaplan, B.B., Heuch, I, et al. (1985). The Collaborative Lipid Research Clinical Family Study: biological and cultural determinants of familial resemblance for plasma lipids and lipoproteins, Genet. Epidemiol.. 2: 227-254.
Nissen, S.E., Tardif, J.C. Nicholls, S.J. for the ILLUSTRATE Investigators (2007). Effect of torcetrapib on the progress of coronary atherosclerosis, New England Journal of Medicine. 356: 1304-16.
Nityasuddhi D. and Böhning D. (2003). Asymptotic properties of the EM algorithm estimate for normal mixture models with component specific variances. Computational Statistics & Data Analysis. 41: 591-601.
Romond, E.H., Perez, E.A., Bryant, J. et al. (2005). Trastuzumab plus chemotherapy for operable HER2-positive breast cancer, New England Journal of Medicine. 353: 1673-84.
Simon R., Maitournam A. (2004). Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical Cancer Research. 10: 6759- 6763.
Simon R. (2008). The use of genomics in clinical trial design. Clinical Cancer Research. 14: 5984-93.
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.
The ALTTO trial. (2008). http://www.cancer.gov/, accessed on March 1, 2008.
U.S. FDA (1998). Decision Summary P980018. Rockville, Maryland, U.S.A.
U.S. FDA (2001). Decision Summary P980024/S001. Rockville, Maryland, U.S.A.
U.S. FDA (2004). Decision Summary P040030. Rockville, Maryland, U.S.A.
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 (2007) Decision Summary k062694. Rockville, Maryland, U.S.A.
U.S. FDA (2007b). Draft Guidance on In Vitro Diagnostic Multivariate Index Assays. The U.S. Food and Drug Administration, Rockville, Maryland, USA.
Varmus H. (2006). The new era in cancer research, Science. 312: 1162-5.
Wu C. F. (1983). On the convergence properties of the EM algorithm. The Annals of Statistics. 11: 95-103.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top