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Application of
adaptive $c$-optimal design for benchmark dose estimation
Marleen Barron and Steven Kim |
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Vol. 18 (2025), No. 2, 351–361
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Abstract |
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In dose-response studies, researchers use regression to describe a dose-response relationship and estimate a benchmark dose. Depending on an experimental design with living animals, we can gain more information about the benchmark dose with a fixed number of animals or the same amount of information with fewer animals. It is not only a mathematical and statistical problem but also an ethical problem. We demonstrate the potential benefit of an adaptive design which starts with a portion of animals to update information then uses remaining animals to maximize information about the benchmark dose. Simulation studies show that even a 4-phase locally optimal design is not robust, but a 2-phase Bayesian optimal design (BOD) is more robust than a 1-phase BOD, and a 4-phase BOD is not necessarily better than a 2-phase BOD. Since more phases require a longer experimental time, a practical recommendation is to use a 2-phase BOD. |
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Keywords
locally optimal design, Bayesian optimal design,
$c$-optimal design, adaptive design, benchmark dose,
logistic regression
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Mathematical Subject Classification
Primary: 62K05
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Milestones
Received: 14 August 2023
Revised: 13 November 2023
Accepted: 13 November 2023
Published: 26 February 2025
Communicated by Sat N. Gupta
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Authors |
| Marleen Barron | |
| Department of Mathematics and
Statistics California State University, Monterey Bay Seaside, CA United States |
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| Steven Kim | |
| Department of Mathematics and
Statistics California State University, Monterey Bay Seaside, CA United States |
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| © 2025 MSP (Mathematical Sciences Publishers). |
