Rejoinder to “Reader Reaction: A Note on the Evaluation of Group Testing Algorithms in the Presence of Misclassification”

Hudgens, Michael G.

doi:10.1111/biom.12387

Cited by 2 publications

(3 citation statements)

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“…Blood banks have used pooled testing to successfully screen out blood infected with transmittable diseases 14‐16 . Pooled testing methods have also been developed and evaluated for identifying acute HIV infection 14,15,17‐29 . These methods rely on a binary biomarker for detection of disease, but a continuous measure is needed to determine if disease is properly suppressed in infected patients.…”

Section: Introductionmentioning

confidence: 99%

“…[14][15][16] Pooled testing methods have also been developed and evaluated for identifying acute HIV infection. 14,15,[17][18][19][20][21][22][23][24][25][26][27][28][29] These methods rely on a binary biomarker for detection of disease, but a continuous measure is needed to determine if disease is properly suppressed in infected patients. Pooled testing methods allowing for a continuous biomarker measure have been developed for assessing treatment failure in patients infected with HIV.…”

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confidence: 99%

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Prediction‐driven pooled testing methods: Application to HIV treatment monitoring in Rakai, Uganda

Brand

May

Nakigozi

et al. 2021

Statistics in Medicine

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Chronic medical conditions often necessitate regular testing for proper treatment. Regular testing of all afflicted individuals may not be feasible due to limited resources, as is true with HIV monitoring in resource-limited settings. Pooled testing methods have been developed in order to allow regular testing for all while reducing resource burden. However, the most commonly used methods do not make use of covariate information predictive of treatment failure, which could improve performance. We propose and evaluate four prediction-driven pooled testing methods that incorporate covariate information to improve pooled testing performance. We then compare these methods in the HIV treatment management setting to current methods with respect to testing efficiency, sensitivity, and number of testing rounds using simulated data and data collected in Rakai, Uganda. Results show that the prediction-driven methods increase efficiency by up to 20% compared with current methods while maintaining equivalent sensitivity and reducing number of testing rounds by up to 70%. When predictions were incorrect, the performance of prediction-based matrix methods remained robust. The best performing method using our motivating data from Rakai was a prediction-driven hybrid method, maintaining sensitivity over 96% and efficiency over 75% in likely scenarios. If these methods perform similarly in the field, they may contribute to improving mortality and reducing transmission in resource-limited settings. Although we evaluate our proposed pooling methods in the HIV treatment setting, they can be applied to any setting that necessitates testing of a quantitative biomarker for a threshold-based decision. K E Y W O R D Smatrix-based testing, mini pool, pooled testing, prediction-based testing, treatment failure INTRODUCTIONProper management of treatable, chronic medical conditions often relies on measurement of one or more biomarkers to assess the effectiveness of current treatment and disease progression. Based on regular blood testing, a patient withThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Introductionmentioning

confidence: 99%

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Prediction‐driven pooled testing methods: Application to HIV treatment monitoring in Rakai, Uganda

Brand

May

Nakigozi

et al. 2021

Statistics in Medicine

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“…As may be expected when breaking with tradition, the proposal generated controversy in the group testing research literature. Both Hudgens and McMahan et al offered rejoinders to Malinovsky et al that disagreed with this new objective function. All three of these works focused only on the Dorfman algorithm in their limited evaluations.…”

Section: Introductionmentioning

confidence: 99%

The objective function controversy for group testing: Much ado about nothing?

Hitt

Bilder

Tebbs

et al. 2019

Statistics in Medicine

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Group testing is an indispensable tool for laboratories when testing high volumes of clinical specimens for infectious diseases. An important decision that needs to be made prior to implementation is determining what group sizes to use. In best practice, an objective function is chosen and then minimized to determine an optimal set of these group sizes, known as the optimal testing configuration (OTC). There are a few options for objective functions, and they differ based on how the expected number of tests, assay characteristics, and testing constraints are taken into account. These varied options have led to a recent controversy in the literature regarding which of two different objective functions is better. In our paper, we examine these objective functions over a number of realistic situations for infectious disease testing. We show that this controversy may be much ado about nothing because the OTCs and corresponding results (eg, number of tests and accuracy) are largely the same for standard testing algorithms in a wide variety of situations.

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Rejoinder to “Reader Reaction: A Note on the Evaluation of Group Testing Algorithms in the Presence of Misclassification”

Abstract: http://onlinelibrary.wiley.com/doi/10.1111/biom.12385/abstract

Cited by 2 publications

References 8 publications

Prediction‐driven pooled testing methods: Application to HIV treatment monitoring in Rakai, Uganda

Prediction‐driven pooled testing methods: Application to HIV treatment monitoring in Rakai, Uganda

The objective function controversy for group testing: Much ado about nothing?

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