Failure of Optimal Design Theory? A Case Study in Toxicology Using Sequential Robust Optimal Design Framework
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913712916398080 |
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| author | Cui, Elvis Han Collins, Michael Munson, Jessica Wong, Weng Kee |
| author_facet | Cui, Elvis Han Collins, Michael Munson, Jessica Wong, Weng Kee |
| contents | This paper presents a quasi-sequential optimal design framework for toxicology experiments, specifically applied to sea urchin embryos. The authors propose a novel approach combining robust optimal design with adaptive, stage-based testing to improve efficiency in toxicological studies, particularly where traditional uniform designs fall short. The methodology uses statistical models to refine dose levels across experimental phases, aiming for increased precision while reducing costs and complexity. Key components include selecting an initial design, iterative dose optimization based on preliminary results, and assessing various model fits to ensure robust, data-driven adjustments. Through case studies, we demonstrate improved statistical efficiency and adaptability in toxicology, with potential applications in other experimental domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00002 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Failure of Optimal Design Theory? A Case Study in Toxicology Using Sequential Robust Optimal Design Framework Cui, Elvis Han Collins, Michael Munson, Jessica Wong, Weng Kee Methodology Applications Computation This paper presents a quasi-sequential optimal design framework for toxicology experiments, specifically applied to sea urchin embryos. The authors propose a novel approach combining robust optimal design with adaptive, stage-based testing to improve efficiency in toxicological studies, particularly where traditional uniform designs fall short. The methodology uses statistical models to refine dose levels across experimental phases, aiming for increased precision while reducing costs and complexity. Key components include selecting an initial design, iterative dose optimization based on preliminary results, and assessing various model fits to ensure robust, data-driven adjustments. Through case studies, we demonstrate improved statistical efficiency and adaptability in toxicology, with potential applications in other experimental domains. |
| title | Failure of Optimal Design Theory? A Case Study in Toxicology Using Sequential Robust Optimal Design Framework |
| topic | Methodology Applications Computation |
| url | https://arxiv.org/abs/2503.00002 |