A Bayesian tool for optimizing sample size in phase I dose-finding trials for head and neck cancer
The development of effective cancer treatments relies heavily on the successful execution of clinical trials, particularly Phase I trials, which are crucial for determining optimal drug dosages. In head and neck cancer, characterized by complex treatment needs and diverse patient responses, optimizi...
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-06-01
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| Series: | Oral Oncology Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772906024003819 |
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| Summary: | The development of effective cancer treatments relies heavily on the successful execution of clinical trials, particularly Phase I trials, which are crucial for determining optimal drug dosages. In head and neck cancer, characterized by complex treatment needs and diverse patient responses, optimizing sample size in these trials is essential for ensuring both safety and efficacy. Traditional sample size planning often uses fixed statistical methods that lack flexibility. In contrast, a Bayesian approach offers a dynamic framework that integrates prior information and continuously updates probabilities with new data, enabling adaptive trial designs. This adaptability allows modifications based on interim results, enhancing patient safety without compromising trial integrity. Bayesian methods improve dose estimation precision, reduce required sample sizes, and facilitate informed decision-making throughout the trial. By continuously updating risk assessments, Bayesian approaches ensure ongoing patient safety and potentially reduce trial costs. This method's ability to provide deeper insights into patient-specific responses supports the development of more personalized therapeutic strategies. As oncology evolves toward precise and personalized treatments, Bayesian methods will play a crucial role in advancing clinical trial design, particularly in Phase I dose-finding trials for head and neck cancer, enhancing the development of next-generation cancer therapies. |
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| ISSN: | 2772-9060 |