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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/124686
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/124686


    Title: 二階段調整雙週期交叉設計實驗之生物對等性檢定之研究
    A Simulation Study of Bioequivalence Test in a Two-stage Cross-over Adaptive Design
    Authors: 黃意珺
    Huang, Yi-Chun
    Contributors: 薛慧敏
    Hsueh, Huey-Ming
    黃意珺
    Huang, Yi-Chun
    Keywords: 二階段調整設計
    2X2交叉設計
    生物對等性檢定
    型一誤差率
    檢定力
    第一階試驗段終止率
    連續型資料
    二元資料
    Binary data
    bioequivalence test
    continuous data
    power
    2X2 cross-over design
    two-stage adaptive design
    type one error rate
    Date: 2019
    Issue Date: 2019-08-07 16:02:04 (UTC+8)
    Abstract: 雙週期調整實驗的目的為提升藥物研發之效率。透過這樣的設計,能使得實驗有機會獲得統計結論提早結束,或者是調整樣本數,得到更適切的結果。為了降低資料變異,每一階段或週期採用交叉實驗。本研究討論雙週期交叉調整設計之生物對等性檢定,我們考慮以雙單尾t檢定(Two one-sided t-tests,簡稱TOST)(Phillips, 1990)來檢定受試者服用兩藥品之血漿濃度之平均水準之對等性。有別於Maurer等人(2018)(Maurer, Jones, & Chen, 2018)考慮二變量非中心化t分佈(Bivariate non-central t-distribution)直接積分以計算檢定力,本篇論文使用電腦模擬常態母體受試者資料以決定生物對等性檢定之檢定力及樣本數。我們透過模擬來研究此雙週期調整實驗之統計性質。我們發現其型一誤差率大多控制在可接受範圍內,但所計算出樣本數過大而導致檢定力高於所要求之水準,具有改善空間。另外,若受試者資料為二元型態,例如受試者服藥後之正面反應與否等,我們研究上述雙週期調整實驗與雙單尾t檢定之適當性。我們使用電腦來模擬伯努力受試者資料以決定該生物對等性檢定之檢定力。最終,模擬實驗的結果顯示在某些情況下,該試驗之型一誤差率稍微膨脹或者檢定力未達要求,而且實驗在第一階段終止率亦不如連續型資料。
    The two-stage adaptive design is developed to increase the efficiency of clinical trials during drug development. Not only that we may obtain a statistical conclusion in the interim analysis and have an early stop of the trial at the first stage, but also that we can have a more adequate sample size re-estimation for the second stage of the experiment. On the other hand, to minimize the data variation, we consider the crossover design of two periods and two sequences in every stage. This research mainly focuses on testing the bioequivalence of a test generic drug to a reference brand-name drug. We consider the two one-sided t-tests (TOST) (Phillips, 1990) to compare the means of the maximum plasma concentration of a patients taking the two different drugs. Instead of integrating a bivariate non-central t-distribution for power calculation (Maurer et al., 2018), we use an R-package to simulate sample data from normal population distributions to calculate the power of the TOST in a cross-over design. The sample size can be determined subsequently. Through a simulation study, we find that the type one error rate of the TOST in the adaptive cross-over design is acceptable mostly. However, the power of the test noticeably exceeds the required level, the sample size estimation tends toward conservatism. Hence, there is a room of improvement for this adaptive design. Besides the data of a continuous end-point, we also investigate the applicability of the TOST in an adaptive cross-over design with the data of a binary end-point. Similarly, we simulate numerous samples of data from Bernoulli population distributions to evaluate the power of the TOST. According to an intensive simulation study, we find that the application of such design with respect to binary data may produce an inflated type I error rate and an insufficient power. Furthermore, the first-stage-stopping-rate is not good as the data of continuous end-point.
    Reference: 吳雅琪. (2011). 臨床試驗樣本數計算簡介. 當代醫藥法規月刊 RegMed, 5.
    Chuang-Stein, C., & Beltangady, M. (2010). FDA draft guidance on adaptive design clinical trials: Pfizer's perspective. Journal of biopharmaceutical statistics, 20(6), 1143-1149.
    Food and Drug Administration, Center for Drug Evaluation and Research(CDER) (2018a). Guidance for Industry : Adaptive Designs for Clinical Trials of Drugs and Biologics.
    Food and Drug Administration, Center for Drug Evaluation and Research(CDER) (2018b). Guidance for Industry : Bioanalytical Method Validation
    Kieser, M., & Rauch, G. (2015). Two‐stage designs for cross‐over bioequivalence trials. Statistics in medicine, 34(16), 2403-2416.
    Labes, D., Schuetz, H., & Lange, B. (2018). Package ‚PowerTOST‘.
    Maurer, W., Jones, B., & Chen, Y. (2018). Controlling the type I error rate in two-stage sequential adaptive designs when testing for average bioequivalence. Statistics in medicine, 37(10), 1587.
    Midha, K. K., & McKay, G. (2009). Bioequivalence; Its History, Practice, and Future. The AAPS Journal, 11(4), 664.
    Phillips, K. F. (1990). Power of the two one-sided tests procedure in bioequivalence. Journal of pharmacokinetics and biopharmaceutics, 18(2), 137-144.
    Pocock, S. J. (1977). Group sequential methods in the design and analysis of clinical trials. 64(2), 191-199.
    Potvin, D., DiLiberti, C. E., Hauck, W. W., Parr, A. F., Schuirmann, D. J., & Smith, R. A. (2008). Sequential design approaches for bioequivalence studies with crossover designs. Pharm Stat, 7(4), 245-262. doi:10.1002/pst.294
    Rosner, B. (2015). Fundamentals of Biostatistics 8th Edition.
    Description: 碩士
    國立政治大學
    統計學系
    106354019
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106354019
    Data Type: thesis
    DOI: 10.6814/NCCU201900537
    Appears in Collections:[統計學系] 學位論文

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