臺灣博碩士論文加值系統

在研發分子標靶藥的早期臨床試驗中，目的在於估計最佳的生物劑量，讓使用該劑量的癌症病患能夠具有最高機率出現藥效，同時，產生劑量限制毒性反應的機率低於設定的目標毒性機率。本文提出穩健的二階段設計，在第一階段中應用現有的第一期穩健臨床試驗設計，決定暫時的最大耐受劑量;第二階段則同時考慮毒性和藥效的反應，調適性的決定病人配置服用的劑量，使得病人能避免接受過毒的劑量且有較高機會獲得較佳的治療。因為此兩階段設計無需假設特定的劑量毒性或劑量藥效模式，是具有實用性的鑑別最佳生物劑量之穩健設計。最後本文在劑量毒性機率與藥效機率的不同組合情境下進行模擬，比較本文所提出的兩階段穩健設計與目前的單階段或兩階段設計的表現。模擬結果顯示本文提出的穩健設計在正確估計最佳生物劑量及合理管控過高劑量的配置方面與其他早期臨床設計具有競爭力，同時在時間成本的控管方面優於單劑量漸增設計。

In this thesis, a two-stage robust design is proposed to find the optimal biological dose (OBD) of a molecular targeted agent (MTA) in an early clinical trial. In the first stage, any model-free design can be used to estimate the maximum toxicity dose (MTD) so that a tentative set of admissible doses is obtained. At the beginning of the second stage, the lowest dose with the most promising efficacy is estimated, and then patients are assigned to receive the adapted dose unless the drug is futility. In fact, in the second stage, the MTD and hence the most promising dose are continuously adjusted. Therefore, patients are safeguarded and have a better chance for receive the OBD. A simulation study is conduct to investigate the performance of the proposed design and some competitive designs under a variety of dose toxicity and dose-response relationships. The results show that the proposed robust design is competitive to previous early clinical trial designs on the OBD estimation and over-toxic dose assignment. Moreover, the proposed two-stage design outperforms the single-dose ascending design on the management of trial time, especially when the efficacy response takes a long time to observe.

摘要 i
Abstract ii
致謝辭 iii
目錄 iv
圖目次 v
第一章 研究動機及目的 1
第二章 文獻回顧 4
2-1 第一期臨床試驗設計 4
2-2 早期臨床試驗設計 8
第三章 穩健二階段早期臨床試驗設計 11
3-1 試驗設計 11
3-2 劑量配置方法 13
第四章 模擬研究 15
4-1模擬研究設計 15
4-2 模擬研究結果 16
第五章 結論與未來研究 27
參考文獻 29

1. Dale, J. R. (1986). Global cross-ratio models for bivariate, discrete, ordered responses. Biometrics, 42(4), 909-917.
2. Hoering, A., Mitchell, A., LeBlanc, M., & Crowley, J. (2013). Early phase trial design for assessing several dose levels for toxicity and efficacy for targeted agents. Clinical Trials, 10(3), 422-429.
3. Ivanova, A. (2003). A new dose‐finding design for bivariate outcomes. Biometrics, 59(4), 1001-1007.
4. Ji, Y., Li, Y., & Nebiyou Bekele, B. (2007). Dose-finding in phase I clinical trials based on toxicity probability intervals. Clinical Trials, 4(3), 235-244.
5. Ji, Y., Liu, P., Li, Y., & Nebiyou Bekele, B. (2010). A modified toxicity probability interval method for dose-finding trials. Clinical Trials, 7(6), 653-663.
6. Lin, R., & Yin, G. (2017). STEIN: A simple toxicity and efficacy interval design for seamless phase I/II clinical trials. Statistics in medicine, 36(26), 4106-4120.
7. Liu, S., & Johnson, V. E. (2016). A robust Bayesian dose-finding design for phase I/II clinical trials. Biostatistics, 17(2), 249-263.
8. Liu, S., & Yuan, Y. (2015). Bayesian optimal interval designs for phase I clinical trials. Journal of the Royal Statistical Society: Series C (Applied Statistics), 64(3), 507-523.
9. Simon, R., Wittes, R. E., & Ellenberg, S. S. (1985). Randomized phase II clinical trials. Cancer treatment reports, 69(12), 1375-1381.
10. Storer, B. E. (1989). Design and analysis of phase I clinical trials. Biometrics, 45(3), 925-937.
11. Thall, P. F., Simon, R. M., & Estey, E. H. (1995). Bayesian sequential monitoring designs for single‐arm clinical trials with multiple outcomes. Statistics in medicine, 14(4), 357-379.
12. Turner, T. R., & Wollan, P. C. (1997). Locating a maximum using isotonic regression. Computational statistics & data analysis, 25(3), 305-320.
13. Zang, Y., & Lee, J. J. (2017). A robust two‐stage design identifying the optimal biological dose for phase I/II clinical trials. Statistics in medicine, 36(1), 27-42.
14. Zang, Y., Lee, J. J., & Yuan, Y. (2014). Adaptive designs for identifying optimal biological dose for molecularly targeted agents. Clinical Trials, 11(3), 319-327.
15. Zohar, S., & O'Quigley, J. (2006). Identifying the most successful dose (MSD) in dose‐finding studies in cancer. Pharmaceutical Statistics: The Journal of Applied Statistics in the Pharmaceutical Industry, 5(3), 187-199.