Pooled testing for HIV prevalence estimation: exploiting the dilution effect

Zenios, Stefanos A.; Wein, Lawrence M.

doi:10.1002/(sici)1097-0258(19980715)17:13<1447::aid-sim862>3.0.co;2-k

Cited by 69 publications

(90 citation statements)

References 12 publications

(3 reference statements)

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“…In most of the applications it is important to take into account the possibility of errors in the tests answers [2,13,[29][30][31], i.e. to consider the faulty-case instead of the gold-standard case analyzed in this work.…”

Section: Perspectivesmentioning

confidence: 99%

Group Testing with Random Pools: Phase Transitions and Optimal Strategy

2008

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The problem of Group Testing is to identify defective items out of a set of objects by means of pool queries of the form "Does the pool contain at least a defective?". The aim is of course to perform detection with the fewest possible queries, a problem which has relevant practical applications in different fields including molecular biology and computer science. Here we study GT in the probabilistic setting focusing on the regime of small defective probability and large number of objects, p → 0 and N → ∞. We construct and analyze one-stage algorithms for which we establish the occurrence of a non-detection/detection phase transition resulting in a sharp threshold, M , for the number of tests. By optimizing the pool design we construct algorithms whose detection threshold follows the optimal scaling M ∝ N p| log p|. Then we consider two-stages algorithms and analyze their performance for different choices of the first stage pools. In particular, via a proper random choice of the pools, we construct algorithms which attain the optimal value (previously determined in Ref. [16]) for the mean number of tests required for complete detection. We finally discuss the optimal pool design in the case of finite p.

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Section: Perspectivesmentioning

confidence: 99%

Group Testing with Random Pools: Phase Transitions and Optimal Strategy

2008

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“…Thus, we suggest the lower bound x L to be one whenever possible. However, too large a value of x U may cause a dilution effect (Zenios & Wein, 1998), impacting the sensitivity or the specificity of the test. Thus, the upper bound x U should be set carefully.…”

Section: The D- and Ds-optimal Designsmentioning

confidence: 99%

Optimal Group Testing Designs for Estimating Prevalence with Uncertain Testing Errors

Huang

Shedden

et al. 2016

Journal of the Royal Statistical Society Series B: Statistical Methodology

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Summary We construct optimal designs for group testing experiments where the goal is to estimate the prevalence of a trait 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. Based on a Chlamydia study in the United States, 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 proposed locally D- and Ds-optimal designs have high efficiencies even when the prespecified values of the parameters are moderately misspecified.

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“…We allow for uncertainty about P y + through its prior, as we describe in Section 2.1, and we introduce a Bayesian measurement error model in Section 2.2 in which the z ’s are no longer measured with perfect accuracy and thus become latent variables in our analysis. Although previous work in this area has accounted for measurement error 15;16;17;18 , knowledge of Y ’s distribution has always been assumed 17;18 .…”

Section: A Bayesian Model For Pooled Datamentioning

confidence: 99%

“…Rather than individual detection though, our goal is prevalence estimation, building on the work of Tu et al 15 . Their method was adapted in Wein and Zenios 16 and in Zenios and Wein 17 to allow inference based on a continuous assay result.…”

Section: Introductionmentioning

confidence: 99%

Estimating the prevalence of transmitted HIV drug resistance using pooled samples

Finucane

Rowley

Paciorek

et al. 2013

Stat Methods Med Res

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In many resource-poor countries, hiv-infected patients receive a standardized antiretroviral (art) cocktail. In these settings, population-level surveillance of drug resistance is needed to characterize the prevalence of resistance mutations and to enable art programs to select the optimal regimen for their local population. The surveillance strategy currently recommended by the World Health Organization is prohibitively expensive in some settings and may not provide a sufficiently precise rendering of the emergence of drug resistance. By using a novel assay on pooled sera samples, we decrease surveillance costs while simultaneously increasing the accuracy of drug resistance prevalence estimates for an important mutation that impacts first-line antiretroviral therapy. We present a Bayesian model for pooled-testing data that garners more information from each resistance assay conducted, compared with individual testing. We expand on previous pooling methods to account for uncertainty about the population distribution of within-subject resistance levels. In addition, our model accounts for measurement error of the resistance assay, and this added uncertainty naturally propagates through the Bayesian model to our inference on the prevalence parameter. We conduct a simulation study that informs our pool size recommendations and that shows that this model renders the prevalence parameter identifiable in instances when an existing non-model-based estimator fails.

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Pooled testing for HIV prevalence estimation: exploiting the dilution effect

Cited by 69 publications

References 12 publications

Group Testing with Random Pools: Phase Transitions and Optimal Strategy

Group Testing with Random Pools: Phase Transitions and Optimal Strategy

Optimal Group Testing Designs for Estimating Prevalence with Uncertain Testing Errors

Estimating the prevalence of transmitted HIV drug resistance using pooled samples

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