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Abstract:
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A group testing study involves collecting samples from multiple individuals, pooling them, and testing them as a group. A realistic cost model for such a study should consider the costs both for collecting the samples, and for testing the assays. One main goal of group testing is to estimate the prevalence of a disease, which can be biased due to misspecified nominal values of testing accuracies. An efficient design should accommodate such inaccuracies. In our work, we theoretically characterized locally optimal group testing designs in this setting. Several simulated examples show that the proposed designs have high efficiency, and are not strongly sensitive to the working parameter specification that is used to obtain the locally optimal design. (Work done jointly with M.-N. L. Huang and K. Shedden.)
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