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 |
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Elsevier
2024-06-01
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| Series: | Oral Oncology Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772906024003819 |
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| author | Sivakamavalli Jeyachandran |
| author_facet | Sivakamavalli Jeyachandran |
| author_sort | Sivakamavalli Jeyachandran |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0519cafb4a94425d81739a3aee3b04da |
| institution | Kabale University |
| issn | 2772-9060 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Oral Oncology Reports |
| spelling | doaj-art-0519cafb4a94425d81739a3aee3b04da2025-01-09T06:16:40ZengElsevierOral Oncology Reports2772-90602024-06-0110100535A Bayesian tool for optimizing sample size in phase I dose-finding trials for head and neck cancerSivakamavalli Jeyachandran0Lab in Biotechnology and Biosignal Transduction, Department of Orthodontics, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, 77, Tamil Nadu, IndiaThe 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.http://www.sciencedirect.com/science/article/pii/S2772906024003819Bayesian toolPhase I trialsDosageTherapeutics |
| spellingShingle | Sivakamavalli Jeyachandran A Bayesian tool for optimizing sample size in phase I dose-finding trials for head and neck cancer Oral Oncology Reports Bayesian tool Phase I trials Dosage Therapeutics |
| title | A Bayesian tool for optimizing sample size in phase I dose-finding trials for head and neck cancer |
| title_full | A Bayesian tool for optimizing sample size in phase I dose-finding trials for head and neck cancer |
| title_fullStr | A Bayesian tool for optimizing sample size in phase I dose-finding trials for head and neck cancer |
| title_full_unstemmed | A Bayesian tool for optimizing sample size in phase I dose-finding trials for head and neck cancer |
| title_short | A Bayesian tool for optimizing sample size in phase I dose-finding trials for head and neck cancer |
| title_sort | bayesian tool for optimizing sample size in phase i dose finding trials for head and neck cancer |
| topic | Bayesian tool Phase I trials Dosage Therapeutics |
| url | http://www.sciencedirect.com/science/article/pii/S2772906024003819 |
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