Optimal group testing designs for estimating prevalence with uncertain testing errors

• 成果类型: Article
• 署名作者: Huang, Shih-Hao; Huang, Mong-Na Lo; Shedden, Kerby; Wong, Weng Kee
• 署名单位: National Sun Yat Sen University; Academia Sinica - Taiwan; University of Michigan System; University of Michigan; University of California System; University of California Los Angeles
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12216
• 发表日期: 2017
• 页码: 1547-1563
• 关键词: locally optimal designs; nonlinear models

摘要

We construct optimal designs for group testing experiments where the goal is to estimate the prevalence of a trait by using a test with uncertain sensitivity and specificity. Using optimal design theory for approximate designs, we show that the most efficient design for simultaneously estimating the prevalence, sensitivity and specificity requires three different group sizes with equal frequencies. However, if estimating prevalence as accurately as possible is the only focus, the optimal strategy is to have three group sizes with unequal frequencies. On the basis of a chlamydia study in the USA we compare performances of competing designs and provide insights into how the unknown sensitivity and specificity of the test affect the performance of the prevalence estimator. We demonstrate that the locally D- and D-s-optimal designs proposed have high efficiencies even when the prespecified values of the parameters are moderately misspecified.