| 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. |
| 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. |
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