Two‐Dimensional Informative Array Testing

摘要: Array-based group testing algorithms for case identification are widely used in infectious disease testing, drug discovery, and genetics. In this paper, we generalize previous statistical work array to account heterogeneity among individuals being tested. We first derive closed-form expressions the expected number of tests (efficiency) misclassification probabilities (sensitivity, specificity, predictive values) two-dimensional a heterogeneous population. then propose two “informative” construction techniques which exploit population ways that can substantially improve efficiency when compared classical approaches regard as homogeneous. Furthermore, useful byproduct our methodology is be estimated on per-individual basis. illustrate new procedures using chlamydia gonorrhea data collected Nebraska part Infertility Prevention Project.