摘要(英) |
This thesis considers early clinical trial designs for the combination of two drugs, which includes at least one Molecularly Targeted Agent (MTA). The efficacy of the MTA may increase at low doses and then plateau at higher doses. Therefore, the purpose of the trial designs is to find the optimal dose combination (ODC) so that patients taking this dose combination achieve the highest efficacy but maintain the target toxicity probability (TTP). Two early clinical trial designs are proposed where the dose combination escalation/de-escalations procedures are based on observations of binary toxicity and efficacy responses. The first design is to extend the single-drug STEIN design to a design for combination drugs, where the dose escalation/de-escalation procedure is constructed by comparing the maximum likelihood estimators (MLEs) of the toxicity and efficacy probabilities with the corresponding thresholds, respectively. The dose escalation/de-escalation procedure of the second design is established by comparing the 100(1-α)% highest posterior probability (HPD) credible interval of the toxicity probability with the TTP and comparing the Bayesian estimate of the efficacy probability with the expected efficacy probability. Therefore, the first design is denoted as mSTEIN and the second one is denoted as CLUB12. Both the designs have rules for early termination and the utility function is used to estimate the ODC when the maximum number of patients is reached. Also, the mSTEIN and CLUB12 are model-assisted designs since there are not any assumption about the specific dose combination-toxicity or dose combination-efficacy model. Finally, a simulation study is carried out to investigate the efficiency of the proposed designs under various combinations of toxicity and efficacy probabilities. The results show that when the efficacy is plateau over the combination doses, the mSTEIN design performs better than the CLUB12 in correctly selecting the ODC and dose-combination assignment to patients. However, when the efficacy probability is ordered, the design efficiency of the CLUB12 design is superior to that of the mSTEIN design. |
參考文獻 |
Cai, C., Yuan, Y., and Ji, Y. (2014) A Bayesian dose-finding design for Oncology clinical trials of combinational Biological Agents. Journal of the Royal Statistical Society: Series C Applied Statistics; 63: 159–173.
Dykstra, R.L. and Robertson, T. (1982) An algorithm for isotonic regression for two or more independent variables. The Annals of Statistics; 10:708-716.
Guo, W., Wang, S.J., Yang, S., Lynn, H. and Ji, Y. (2017) A Bayesian interval dose-finding design addressing Ockham’s razor: MTPI-2. Contemporary Clinical Trials; 58:23-33.
Huang, X., Biswas, S., Oki, Y., Issa, J.P., Berry, D.A. (2007) A parallel phase I/II clinical trial design for combination therapies. Biometrics; 63:429-436.
Liu, S. and Yuan, Y. (2015) Bayesian optimal interval designs for phase I clinical trials. Journal of the Royal Statistical Society: Series C Applied Statistics; 64:507-523.
Liu, S. and Johnson, V.E. (2016) A robust Bayesian dose-finding design for phase I/II clinical trials. Biostatistics; 17:249-263.
Lin, R. and Yin, G. (2017) STEIN: A simple toxicity and efficacy interval design for seamless phase I/II clinical trials. Statistics in medicine; 36:4106-4120.
Lin, R. and Yin, G. (2017) Bayesian optimal interval design for dose finding in drug-combination trials. Statistical methods in medical research; 26: 2155- 2167.
Pan, H., Lin, R., Zhou, Y., Yuan, Y. (2020) Keyboard design for phase I drug-combination trials. Contemporary Clinical Trials; 92:105972.
Riviere, M.K., Yuan, Y., Dubois, F., Zohar, S. (2015) A Bayesian dose-finding design for clinical trials combining a cytotoxic agent with a molecularly targeted agent. Journal of the Royal Statistical Society: Series C; 64:215-229.
Yuan, Y. and Yin, G. (2011) Bayesian phase I/II adaptive randomized Oncology trials with combined drugs. Ann Appl Stat; 5: 924–942.
許嘉雯,「混和藥物之模型輔助早期臨床試驗設計」,國立中央大學,碩士論文,民國110年。 |