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研究生： 張凱茵
Chang, Kai-Yin
論文名稱： 比較不同基因多型性間的藥物效用
Comparing drug efficacy between subgroups defined by genetic polymorphisms
指導教授： 杜宜軒
Tu, Yi-Hsuan
學位類別： 碩士
Master
系所名稱： 管理學院 - 統計學系
Department of Statistics
論文出版年： 2012
畢業學年度： 100
語文別： 英文
論文頁數： 52
中文關鍵詞： 多重比較 、次群組分析 、基因多型性
外文關鍵詞： multiple comparisons, subgroup analyses, genetic polymorphisms
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    • 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。

    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.

    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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