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系統識別號 U0026-0907201213560200
論文名稱(中文) 比較不同基因多型性間的藥物效用
論文名稱(英文) Comparing drug efficacy between subgroups defined by genetic polymorphisms
校院名稱 成功大學
系所名稱(中) 統計學系碩博士班
系所名稱(英) Department of Statistics
學年度 100
學期 2
出版年 101
研究生(中文) 張凱茵
研究生(英文) Kai-Yin Chang
學號 r26991038
學位類別 碩士
語文別 英文
論文頁數 52頁
口試委員 指導教授-杜宜軒
口試委員-黃錦輝
口試委員-吳雅琪
中文關鍵字 多重比較  次群組分析  基因多型性 
英文關鍵字 multiple comparisons  subgroup analyses  genetic polymorphisms 
學科別分類
中文摘要 除了整體研究之外,次群組分析在臨床試驗中變得越來越重要。次群組分析可以幫助分辨哪些次群組在臨床試驗中能得到藥物或治療的好處。預防不穩定心絞痛復發的保栓通試驗是一個隨機、雙盲且以安慰劑為控制組的試驗。這個試驗的目的在於比較保栓通合併阿斯匹靈和安慰劑合併阿斯匹靈的藥效主要是針對患有急性冠心症非ST段上升的病人。CYP2C19是一個具有高度基因多型性的肝臟代謝酵素而且參與許多藥物的代謝包含保栓通。保栓通的藥效是否會隨CYP2C19的代謝表現型不同而有所不同是我們有興趣的議題。我們提出平均數多重比較的方法,比較每種代謝表現型的藥物效應和它們的平均藥物效應。平均數多重比較的方法可以有效控制整體型I誤差。我們同時也延伸Alosh和Huque在2009年所發表的方法並且考慮一致性的概念。一致性的意義在於不同的次群組之間的治療效應不能是相反的。有鑑於一致性,我們將整體和次群組皆列入考慮,並且對所有代謝表現型的藥物效應有興趣。這個延伸的方法能有效控制整體型I誤差。
英文摘要 In addition to overall study population, subgroup analyses in clinical trials are becoming increasingly important. Subgroup analyses can help distinguish which subgroups benefit from certain drugs or treatments in clinical trials. The CURE trial is a randomized, double-blind, placebo-controlled trial. The purpose of the CURE trial is to compare clopidogrel plus aspirin with placebo plus aspirin for patients with acute coronary syndromes without ST-segment elevation. CYP2C19 is a highly polymorphic liver enzyme and involved with the metabolism of many drugs, including clopidogrel. One of our interests is to determine whether the performance of clopidogrel will differ among different CYP2C19 metabolizer phenotypes. We propose the multiple comparisons with the mean as a method to compare each effect of metabolizer phenotype with the average of all effects of metabolizer phenotypes, and the multiple comparisons with the mean is a method which can efficiently control familywise error rate. We also extend the method from Alosh and Huque (2009), and consider the concept of consistency. The meaning of consistency lies in that the treatment effect can not be in the opposite direction in different subgroups. In terms of consistency, we take overall study population and all subgroups into consideration, and are interested in all effects of metabolizer phenotypes. The extension method can efficiently control the familywise error rate.

論文目次 1 Introduction.............................................1

2 Multiple comparisons with the Mean.......................3
2.1 One-Sided Multiple Comparisons with the Mean..........4
2.1.1 Unbalanced One-Way Model.........................4
2.1.2 Balanced One-Way Model...........................6
2.2 Two-Sided Multiple Comparisons with the Mean..........8
2.2.1 Unbalanced One-Way Model.........................8
2.2.2 Balanced One-Way Model...........................9

3 Compare the multiple comparisons with the mean with the
analysis of means........................................9
3.1 Unbalanced One-Way Model.............................10
3.2 Balanced One-Way Model...............................11

4 Example of multiple comparisons with the mean...........12

5 Extension of the method from Alosh and Huque (2009).....15
5.1 The method of Alosh and Huque (2009).................15
5.2 The extension method.................................16
5.2.1 Control of the FWER.............................17
5.2.2 Derivation of the significance level for all
subgroups.......................................19
5.2.3 Derivation of the power for detecting the
treatment effect of all subgroups...............23
5.3 Example..............................................33

6 Concluding remarks......................................34

7 Future works............................................35

References................................................37

Appendix A................................................39
Appendix B................................................41
Appendix C................................................42
Appendix D................................................45
Appendix E................................................48
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