Nested Group Testing Procedure.

Group testing Negative predictive value Positive predictive value Retest

Journal

Communications in mathematics and statistics
ISSN: 2194-6701
Titre abrégé: Commun Math Stat
Pays: Germany
ID NLM: 101674997

Informations de publication

Date de publication:
01 Oct 2022
Historique:
received: 09 05 2021
revised: 27 06 2021
accepted: 13 10 2021
entrez: 10 10 2022
pubmed: 11 10 2022
medline: 11 10 2022
Statut: aheadofprint

Résumé

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.

Identifiants

pubmed: 36213843
doi: 10.1007/s40304-021-00269-0
pii: 269
pmc: PMC9525165
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1-31

Informations de copyright

© School of Mathematical Sciences, University of Science and Technology of China and Springer-Verlag GmbH Germany, part of Springer Nature 2022.

Références

Am J Hum Genet. 2000 Oct;67(4):1036-9
pubmed: 10986050
Stat Methods Med Res. 2019 Jan;28(1):211-222
pubmed: 28797203
J Am Stat Assoc. 2010 Sep 1;105(491):942-955
pubmed: 21113353
Allergy. 2010 Mar;65(3):327-32
pubmed: 19860790
Biometrics. 2012 Mar;68(1):287-96
pubmed: 21762119
Biometrics. 2020 Dec;76(4):1147-1156
pubmed: 32083733
Stat Med. 2015 Nov 30;34(27):3606-21
pubmed: 26173957
J R Stat Soc Ser C Appl Stat. 2012 Mar 1;61(2):277-290
pubmed: 25035521
Biometrics. 2007 Dec;63(4):1152-63
pubmed: 17501946
J Clin Microbiol. 2015 Mar;53(3):1002-4
pubmed: 25552360
Biometrics. 2012 Sep;68(3):793-804
pubmed: 22212007
Biometrics. 2017 Jun;73(2):656-665
pubmed: 27657666
J Viral Hepat. 2018 Jun;25(6):718-723
pubmed: 29316078
Stat Methods Med Res. 2016 Apr;25(2):917-35
pubmed: 23376965
Biometrics. 2019 Mar;75(1):13-23
pubmed: 30267535
J Virol Methods. 2021 Mar;289:114044
pubmed: 33316285
Stat Med. 2020 Mar 15;39(6):687-697
pubmed: 31758594
Malar J. 2014 Dec 18;13:509
pubmed: 25522751
Arthritis Rheum. 1989 Jan;32(1):82-5
pubmed: 2912466
Comput Stat Data Anal. 2018 Jun;122:156-166
pubmed: 29977101

Auteurs

Wenjun Xiong (W)

School of Mathematics and Statistics, Guangxi Normal University, Guilin, 541004 People's Republic of China.

Juan Ding (J)

Department of Information and Computing Science, College of Sciences, Hohai University, Nanjing, 210098 People's Republic of China.

Wei Zhang (W)

LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190 People's Republic of China.

Aiyi Liu (A)

Biostatisics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health, Bethesda, 20817 USA.

Qizhai Li (Q)

LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190 People's Republic of China.
University of Chinese Academy of Sciences, Beijing, 100049 People's Republic of China.

Classifications MeSH