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. 2021 Jul 30;40(17):3865-3880.
doi: 10.1002/sim.9003. Epub 2021 Apr 28.

Is group testing ready for prime-time in disease identification?

Gregory Haber  1 , Yaakov Malinovsky  2 , Paul S Albert  1
Affiliations

Is group testing ready for prime-time in disease identification?

Gregory Haber et al. Stat Med. .

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.

Keywords: disease screening; epidemiology; group testing; measurement error.

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Figures

FIGURE 1
FIGURE 1
Plot of sensitivity function, Se(k)=fH(p, k, d) for p=0.1 for various values of k and d
FIGURE 2
FIGURE 2
Barplots showing required validation study size, N, total number of assay tests in validation study, TV, and minimum population size to see a benefit from group testing, N* for various underlying true sensitivity functions. The bottom axes are values of p
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