Literature DB >> 36213843

Nested Group Testing Procedure.

Wenjun Xiong1, Juan Ding2, Wei Zhang3, Aiyi Liu4, Qizhai Li3,5.   

Abstract

We investigated the false-negative, true-negative, false-positive, and true-positive predictive values from a general group testing procedure for a heterogeneous population. We show that its false (true)-negative predictive value of a specimen is larger (smaller), and the false (true)-positive predictive value is smaller (larger) than that from individual testing procedure, where the former is in aversion. Then we propose a nested group testing procedure, and show that it can keep the sterling characteristics and also improve the false-negative predictive values for a specimen, not larger than that from individual testing. These characteristics are studied from both theoretical and numerical points of view. The nested group testing procedure is better than individual testing on both false-positive and false-negative predictive values, while retains the efficiency as a basic characteristic of a group testing procedure. Applications to Dorfman's, Halving and Sterrett procedures are discussed. Results from extensive simulation studies and an application to malaria infection in microscopy-negative Malawian women exemplify the findings. © School of Mathematical Sciences, University of Science and Technology of China and Springer-Verlag GmbH Germany, part of Springer Nature 2022.

Entities:  

Keywords:  Group testing; Negative predictive value; Positive predictive value; Retest

Year:  2022        PMID: 36213843      PMCID: PMC9525165          DOI: 10.1007/s40304-021-00269-0

Source DB:  PubMed          Journal:  Commun Math Stat        ISSN: 2194-671X


  20 in total

1.  The efficiency of pooling in the detection of rare mutations.

Authors:  J L Gastwirth
Journal:  Am J Hum Genet       Date:  2000-10       Impact factor: 11.025

2.  Comparison of group testing algorithms for case identification in the presence of test error.

Authors:  Hae-Young Kim; Michael G Hudgens; Jonathan M Dreyfuss; Daniel J Westreich; Christopher D Pilcher
Journal:  Biometrics       Date:  2007-05-14       Impact factor: 2.571

3.  Cost-effective pooling of DNA from nasopharyngeal swab samples for large-scale detection of bacteria by real-time PCR.

Authors:  Sophie Edouard; Elsa Prudent; Philippe Gautret; Ziad A Memish; Didier Raoult
Journal:  J Clin Microbiol       Date:  2014-12-31       Impact factor: 5.948

4.  A general regression framework for group testing data, which incorporates pool dilution effects.

Authors:  Dewei Wang; Christopher S McMahan; Colin M Gallagher
Journal:  Stat Med       Date:  2015-07-14       Impact factor: 2.373

5.  Improved matrix pooling.

Authors:  Wenjun Xiong; Juan Ding; Yuanzhen He; Qizhai Li
Journal:  Stat Methods Med Res       Date:  2017-08-10       Impact factor: 3.021

6.  Informative Dorfman screening.

Authors:  Christopher S McMahan; Joshua M Tebbs; Christopher R Bilder
Journal:  Biometrics       Date:  2011-07-15       Impact factor: 2.571

7.  Informative Retesting.

Authors:  Christopher R Bilder; Joshua M Tebbs; Peng Chen
Journal:  J Am Stat Assoc       Date:  2010-09-01       Impact factor: 5.033

8.  Group testing in heterogeneous populations by using halving algorithms.

Authors:  Michael S Black; Christopher R Bilder; Joshua M Tebbs
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2012-03-01       Impact factor: 1.864

9.  Determining the negative predictive value of provocation tests with beta-lactams.

Authors:  P Demoly; A Romano; C Botelho; L Bousquet-Rouanet; F Gaeta; R Silva; G Rumi; J Rodrigues Cernadas; P J Bousquet
Journal:  Allergy       Date:  2009-10-26       Impact factor: 13.146

10.  Nonparametric estimation of distributions and diagnostic accuracy based on group-tested results with differential misclassification.

Authors:  Wei Zhang; Aiyi Liu; Qizhai Li; Paul S Albert
Journal:  Biometrics       Date:  2020-03-05       Impact factor: 1.701

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