博碩士論文 105225008 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:1 、訪客IP:35.173.57.84
姓名 李亞晟(Ya-Cheng Lee)  查詢紙本館藏   畢業系所 統計研究所
論文名稱 第一期臨床試驗之貝氏調適設計
相關論文
★ 藥物最低有效劑量之無母數鑑別★ 根據貝氏檢定建構的第一期臨床試驗設計
★ 強餘震之即時貝氏預測★ 鑑別最佳添加藥物劑量的兩階段早期臨床試驗設計
★ 臺灣地區地下水品質之統計研究★ 右設限存活資料之下每日可服劑量之研究
★ 集集餘震之統計研究★ 多群資料下最低有效劑量之聯合鑑別
★ 最大餘震規模之統計分析★ 最大餘震發生時間之統計分析
★ 地震預測之統計分析★ 加權Kaplan-Meier統計量之推廣
★ 鑑別藥物最低有效劑量之檢定★ 餘震序列RJ模型之貝氏分析
★ 藥物最低有效劑量之穩健鑑別★ 台灣強震之時間與規模預測模型
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中) 研發癌症新藥的第一期臨床試驗之主要目的是估計最大耐受劑量,其中服用該劑量的病患產生劑量限制毒性的機率最接近目標毒性機率。其次考量在劑量升降的試驗過程中,不宜分派過多病患服用無藥效的劑量,更要避免讓病患承擔過高的中毒風險,因此希望降低施予病患過低或過高劑量的機會。本文建議根據最大耐受劑量的後驗分布中位數建立劑量升降過程。當試驗病患人數用盡後,本文則建議參考最大耐受劑量後驗分布中位數與眾數選擇最大耐受劑量,以便提供第二期臨床試驗使用。因為本文研究的是根據後驗分布中位數及眾數的第一期臨床試驗設計,因此將此設計方法記作MEMO。本文針對MEMO試驗設計進行劑量的配置設計,使得MEMO試驗設計在應用上更加穩健,另外也針對晚發毒性的情形推廣MEMO試驗設計,獲得TITE-MEMO試驗設計。最後本文在各種不同的劑量毒性關係下,藉由模擬研究探討所提出的方法相對於改良毒性機率區間、連續重評估方法與改良嚴格控管過度劑量方法之表現。研究顯示,本文提出的方法在劑量升降過程中能合理的控制施予病患過高劑量的可能性,也能提供相對靠近真實最大耐受劑量的劑量給未來第二期的臨床試驗使用。
摘要(英) The major purpose of a phase I clinical trial is to estimate the maximum tolerated dose (MTD) of the drug at which the probability of the dose-limiting toxicity (DLT) is closest the targeted toxicity probability (TTP). Meanwhile, it is important to avoid assigning patients to less therapeutic doses and protect patients from overdosing in the dose escalation procedure. This article considers constructing the dose-escalation procedure based on the posterior medians of the MTD. When the maximum number of patients is reached, the MTD is then recommended by taking inference of the posterior median and mode of the MTD. Since the design involves both the posterior median and mode of the MTD, it is denoted by MEMO. The working doses for the MEMO design are calibrated based on the empirical power model for the dose-toxicity relationship. Moreover, the proposed design can be generalized to be TITE-MEMO for late-onset toxicity when data of time-to-event are available. A simulation study is finally conducted to compare the relative performance of the proposed designs to some competitive designs, for example, modified toxicity probability interval method, continual reassessment method and modified escalation with overdose control, dose assignment and MTD selection under a variety of scenarios of dose-toxicity relationship. The proposed designs generally produce a dose assignment that appropriately reduce the risk of overdose and give a recommendation of feasible MTD for the further phase II trial.
關鍵字(中) ★ 最大耐受劑量
★ 貝氏方法
★ 第一期臨床試驗
★ 劑量配置設計
★ 晚發毒性
關鍵字(英)
論文目次 摘要 I
Abstract II
致謝詞 III
目錄 IV
圖目次 VI
表目次 VII
第一章 研究動機及目的 1
第二章 文獻回顧 4
2-1 改良毒性機率區間 4
2-2 連續重評估方法 5
2-3 嚴格控管過度劑量方法 10
2-4 毒性相依實用界線方法 12
第三章 貝氏調適試驗設計 14
3-1 試驗設計 14
3-2 劑量配置設計 17
3-3 晚發毒性試驗設計 18
3-4 試驗設計的特例 19
第四章 模擬研究 21
4-1 模擬研究之設計 21
4-2 模擬研究結果 22
4-2-1 完整資料結果 22
4-2-2 晚發毒性結果 23
第五章 結論 25
參考文獻 27
附圖 29
附表 39
參考文獻 1. Babb J, Rogatko A and Zack S (1998). Cancer phase I clinical trials: efficient dose escalation with overdose control. Statistics in Medicine, 17, 1103-1120.
2. Cheung YK (2011). Dose Finding by the Continual Reassessment Method. Taylor & Francis US.
3. Cheung YK and Chappell R (2000). Sequential designs for phase I clinical trials with late-onset toxicities. Biometrics, 56, 1177-1182.
4. Faries D (1994). Practical modifications of the continual reassessment method for Phase I cancer clinical trials. Journal of Biopharmaceutical Statistics, 4, 147–164.
5. Goodman SN, Zahurak ML and Piantadosi S (1995). Some practical improvements in the continual reassessment method for Phase I studies. Statistics in Medicine, 14, 1149–1161.
6. Hastings WK (1970). Monte Carlo Sampling Methods Using Markov Chains and Their Applications. Biometrika, 57, 97-109.
7. Horton BJ, Wages NA and Conaway MR (2016). Performance of toxicity probability interval based designs in contrast to the continual reassessment method. Statistics in Medicine, 36, 291-300.
8. Ji Y, Liu P, Li Y and Bekele BN (2010). A modified toxicity probability interval method for dose-finding trials. Clinical Trials, 7,653-663.
9. Lee SM and Cheung YK (2009). Model calibration in the continual reassessment method. Clinical Trials, 6, 227-238
10. Lin Y and Shih WJ (2001). Statistical properties of the traditional algorithm-based designs for phase I cancer clinical trials. Biostatistics, 2, 203-215.
11. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, and Teller E (1953). Equations of State Calculations by Fast Computing Machines. Journal of Chemical Physics, 21, 1087-1092.
12. O’Quigley J, Pape M and Fisher L (1990). Continual Reassessment Method: A Practical Design for Phase 1 Clinical Trials in Cancer. Biometrics, 46(1), 33-48.
13. Paoletti X and Kramar A (2009). A comparison of model choices for the continual reassessment method in phase I cancer trials. Statistics in Medicine, 28, 3012–3028.
14. Paoletti X, O’Quigley J, Maccario J (2004). Design efficiency in dose-finding studies. Computational Statistics & Data Analysis, 45(2), 197-214
15. Wheeler GM, Sweeting MJ and Mander AP (2017). Toxicity-dependent feasibility bounds for the escalation with overdose control approach in phase I cancer trials. Statistics in Medicine, 36, 2499–2513.
16. 鍾佳儒 (2017). Bayesian test-based designs for phase I clinical trials. Master’s thesis, 國立中央大學.
指導教授 陳玉英(Yuh-Ing Chen) 審核日期 2018-7-26
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明