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研究生:李冠霆
研究生(外文):Kuan-Ting Lee
論文名稱: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
論文名稱(外文):Statistical analysis of censored endpoints under the Cox proportional hazard model for evaluation of targeted drug products under the enrichment design
指導教授:蔡政安蔡政安引用關係劉仁沛劉仁沛引用關係
指導教授(外文):Chen-An TsaiJen-Pei Liu
口試委員:季瑋珠林志榮
口試委員(外文):Wei-Chu ChieJr-Rung Lin
口試日期:2015-06-30
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:農藝學研究所生物統計組
學門:醫藥衛生學門
學類:其他醫藥衛生學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:83
中文關鍵詞: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
外文關鍵詞:Targeted clinical trialsEnrichment designcensored dataEM algorithmCox proportion hazard model
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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。

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.

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



REFERENCES
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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:
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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.
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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


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