Literature DB >> 7846399

Studies of AIDS and HIV surveillance. Screening tests: can we get more by doing less?

X M Tu1, E Litvak, M Pagano.   

Abstract

Estimating the prevalence of the human immunodeficiency virus (HIV) in a group is challenging; this is especially so when the prevalence is small. One reason is that the presence of measurement errors resulting from the limited precision of tests makes estimation, using traditional methods, impossible in some screening situations. Measurement error is real, ignoring it leads to severe bias, and inference about the prevalence becomes unsatisfactory. Indeed, in a low prevalence situation the expected number of false positives is very high, often even higher than the number of true positives. The second reason is that in the low prevalence areas the large sample is needed in order to obtain non-zero estimate. This is usually a very costly, and often unrealistic, solution. This paper considers the advantages and disadvantages of pooled testing as an alternative solution to this problem. We show that by pooling sera samples we not only achieve a cost saving but also, which is counterintuitive, an increase in the estimation accuracy. We also discuss the statistical issues associated with the resulting estimator.

Entities:  

Mesh:

Year:  1994        PMID: 7846399     DOI: 10.1002/sim.4780131904

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  13 in total

1.  Optimal pooled testing.

Authors:  Brett A Saraniti
Journal:  Health Care Manag Sci       Date:  2006-05

2.  Regression models for group testing data with pool dilution effects.

Authors:  Christopher S McMahan; Joshua M Tebbs; Christopher R Bilder
Journal:  Biostatistics       Date:  2012-11-28       Impact factor: 5.899

3.  Misclassified group-tested current status data.

Authors:  L C Petito; N P Jewell
Journal:  Biometrika       Date:  2016-12-08       Impact factor: 2.445

4.  Efficacy of soaking in 70% isopropyl alcohol on aerobic bacterial decontamination of surgical instruments and gloves for serial mouse laparotomies.

Authors:  Jessica N Keen; MaryKay Austin; Li-Shan Huang; Susan Messing; Jeffrey D Wyatt
Journal:  J Am Assoc Lab Anim Sci       Date:  2010-11       Impact factor: 1.232

5.  Prevalence estimation subject to misclassification: the mis-substitution bias and some remedies.

Authors:  Zhiwei Zhang; Chunling Liu; Sungduk Kim; Aiyi Liu
Journal:  Stat Med       Date:  2014-07-18       Impact factor: 2.373

6.  Group testing case identification with biomarker information.

Authors:  Dewei Wang; Christopher S McMahan; Joshua M Tebbs; Christopher R Bilder
Journal:  Comput Stat Data Anal       Date:  2018-02-01       Impact factor: 1.681

7.  Estimating the prevalence of transmitted HIV drug resistance using pooled samples.

Authors:  Mariel M Finucane; Christopher F Rowley; Christopher J Paciorek; Max Essex; Marcello Pagano
Journal:  Stat Methods Med Res       Date:  2013-02-01       Impact factor: 3.021

8.  Comparing Multiple Sensitivities and Specificities with Different Diagnostic Criteria: Applications to Sexual Abuse and Sexual Health Research.

Authors:  Q Yu; W Tang; Y Ma; S A Gamble; X M Tu
Journal:  Comput Stat Data Anal       Date:  2008-09       Impact factor: 1.681

9.  Improved confidence intervals of a small probability from pooled testing with misclassification.

Authors:  Chunling Liu; Aiyi Liu; Bo Zhang; Zhiwei Zhang
Journal:  Front Public Health       Date:  2013-10-07

10.  Pooled PCR testing strategy and prevalence estimation of submicroscopic infections using Bayesian latent class models in pregnant women receiving intermittent preventive treatment at Machinga District Hospital, Malawi, 2010.

Authors:  Zhiyong Zhou; Rebecca Mans Mitchell; Julie Gutman; Ryan E Wiegand; Dyson A Mwandama; Don P Mathanga; Jacek Skarbinski; Ya Ping Shi
Journal:  Malar J       Date:  2014-12-18       Impact factor: 2.979

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