Optimal Group Testing Design with Cost Considerations and Dilution Effects

Abstract

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 test sensitivity and specificity. An efficient design should accommodate such inaccuracies. In our series of works, we derive locally optimal designs in this setting, and characterize their theoretical properties. We also provide a guaranteed algorithm for constructing the designs on discrete design spaces. Several simulated examples based on a chlamydia study in the USA 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, K. Shedden, and W. K. Wong.) ? Key words and phrases: bias-variance trade-off; budget constrained design; dilution effect model; D-optimality; group testing