Featured Article from Statistics in Medicine on Group Testing in Disease Indentification

Statistics in Medicine has just published a Featured Article on group testing in disease identification in issue 40:17:

Is group testing ready for prime-time in disease identification? by Gregory Haber, Yaakov Malinovsky and Paul S. Albert, Statistics in Medicine2021403865– 3880https://doi.org/10.1002/sim.9003

Abstract: Large-scale disease screening is a complicated process in which high costs must be balanced against pressing public health needs. When the goal is screening for infectious disease, one approach is group testing in which samples are initially tested in pools and individual samples are retested only if the initial pooled test was positive. Intuitively, if the prevalence of infection is small, this could result in a large reduction of the total number of tests required. Despite this, the use of group testing in medical studies has been limited, largely due to skepticism about the impact of pooling on the accuracy of a given assay. While there is a large body of research addressing the issue of testing errors in group testing studies, it is customary to assume that the misclassification parameters are known from an external population and/or that the values do not change with the group size. Both of these assumptions are highly questionable for many medical practitioners considering group testing in their study design. In this article, we explore how the failure of these assumptions might impact the efficacy of a group testing design and, consequently, whether group testing is currently feasible for medical screening. Specifically, we look at how incorrect assumptions about the sensitivity function at the design stage can lead to poor estimation of a procedure’s overall sensitivity and expected number of tests. Furthermore, if a validation study is used to estimate the pooled misclassification parameters of a given assay, we show that the sample sizes required are so large as to be prohibitive in all but the largest screening programs.

The article and the accompanying commentaries and rejoinder are all free to read:

Bilder, CRTebbs, JMMcMahan, CSDiscussion on “Is group testing ready for prime-time in disease identification”Statistics in Medicine2021403881– 3886https://doi.org/10.1002/sim.8988

Biggerstaff, BJDiscussion of “Is group testing ready for prime-time in disease Identification?” by Haber, Malinovsky, and Albert, Statistics in Medicine, 2021Statistics in Medicine2021403887– 3888https://doi.org/10.1002/sim.8989

Johnson, W.O. (2021), Comment on “Is Group Testing Ready for Prime Time in Disease Identification?”. Statistics in Medicine, 40: 3889-3891. https://doi.org/10.1002/sim.9078

Haber, GMalinovsky, YAlbert, PSRejoinder to discussion on Is group testing ready for prime-time in disease identification?Statistics in Medicine2021403892– 3894https://doi.org/10.1002/sim.9033


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