Abstract
We present a case study of a Phase 1 oncology dose-escalation trial utilizing modified Continual Reassessment Method (CRM). Learning about the dose–toxicity relationship and choosing the correct Maximum Tolerated Dose (MTD) to take forward into Phase II is one of the most challenging research questions in Phase 1 oncology trials. CRM is a Bayesian adaptive design targeting a specific Dose Limiting Toxicity (DLT) rate, e.g., 25 %. Similar to the traditional 3 + 3 designs used in oncology Phase 1 trials, learning about drug’s toxicity profile with CRM occurs in real time. However, since CRM algorithm incorporates dose–toxicity modeling in the learning process, its ability to identify the correct Maximum Tolerated Dose is substantially improved, compared to the traditional 3 + 3 design. Such design also results in more patients being allocated to tolerable doses with therapeutic potential than would be the case in a more traditional 3 + 3 dose-escalation trial. This trial was designed and executed using a custom-developed and validated software package which helped to alleviate substantial increase in overhead cost typically associated with planning and implementation of such designs. We present the whole “story” of the trial from beginning to end, including selection of study design, assessment of its operating characteristics via simulations, execution, study results, and lessons learned.
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Acknowledgements
The authors sincerely thank Michael Krams for his pioneering work in making this adaptive trial happen; Vlad Dragalin and Amar Sharma for their instrumental help in designing this trial; Tom Parke for guidance with software; Charles Zacharchuk and Robert Millham for their leadership role in executing this trial; and Stephanie Green for careful review of the manuscript and insightful feedback.
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Perevozskaya, I., Han, L., Pierce, K. (2014). Continual Reassessment Method for a First-in-Human Trial: From Design to Trial Implementation. In: He, W., Pinheiro, J., Kuznetsova, O. (eds) Practical Considerations for Adaptive Trial Design and Implementation. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1100-4_19
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DOI: https://doi.org/10.1007/978-1-4939-1100-4_19
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