BIOSTATISTICS SEMINAR
HIERARCHICAL GROUP TESTING FOR MULTIPLE
INFECTIONS
Joshua M. Tebbs
Professor
Department of Statistics
University of South Carolina
Abstract
Group testing, where individuals are tested initially in pools, is often used to screen
a large number of individuals for rare diseases. Triggered by the recent development
of assays that detect multiple infections, large-scale screening programs now involve
testing individuals in pools for multiple infections simultaneously. Tebbs, McMahan,
and Bilder (2013, Biometrics) recently evaluated the performance of a two-stage
hierarchical algorithm used to screen for chlamydia and gonorrhea as part of the
Infertility Prevention Project in the United States. In this article, we generalize this
work to accommodate a larger number of stages. To derive the operating
characteristics of higher-stage hierarchical algorithms with more than one infection,
we view the pool decoding process as a time-inhomogeneous, finite-state Markov
chain. Taking this conceptualization enables us to derive closed-form expressions
for the expected number of tests and classification accuracy rates in terms of
transition probability matrices. When disease probabilities are small, we offer
compelling evidence that higher-stage algorithms can provide significant savings
when screening a population for multiple infections. We also demonstrate that if
prevalence estimation is an additional goal, two-stage algorithms provide most of the
benefits in terms of estimation efficiency.
The Johns Hopkins Bloomberg School of Public Health
Department of Biostatistics, Monday, September 29, 2014, 12:15-1:15
Room W3008, School of Public Health (Refreshments: 12:00-12:15)
Note: Taking photos during the seminar is prohibited
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