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Pooled‐testing procedures for screening high volume clinical specimens in heterogeneous populations
Author(s) -
Bilder Christopher R.,
Tebbs Joshua M.
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5334
Subject(s) - computer science , statistics , set (abstract data type) , multiple comparisons problem , medicine , mathematics , programming language
Pooled testing is a procedure commonly used to reduce the cost of screening a large number of individuals for infectious diseases. In its simplest form, pooled testing works by compositing a set of individual specimens (e.g., blood or urine) into a common pool. If the pool tests negative, all individuals within it are diagnosed as negative. If the pool tests positive, retesting is needed to decode the positive individuals from the negative individuals. Traditionally, pooled testing has assumed that each individual has the same probability of being positive. However, this assumption is often unrealistic, especially when known risk factors can be used to measure distinct probabilities of positivity for each individual. In this paper, we investigate new pooled‐testing algorithms that exploit the heterogeneity among individual probabilities and subsequently reduce the total number of tests needed, while maintaining accuracy levels similar to standard algorithms that do not account for heterogeneity. We apply these algorithms to data from the Infertility Prevention Project, a nationally implemented program supported by the Centers for Disease Control and Prevention. Copyright © 2012 John Wiley & Sons, Ltd.

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