Literature DB >> 21833347

binGroup: A Package for Group Testing.

Christopher R Bilder1, Boan Zhang, Frank Schaarschmidt, Joshua M Tebbs.   

Abstract

When the prevalence of a disease or of some other binary characteristic is small, group testing (also known as pooled testing) is frequently used to estimate the prevalence and/or to identify individuals as positive or negative. We have developed the binGroup package as the first package designed to address the estimation problem in group testing. We present functions to estimate an overall prevalence for a homogeneous population. Also, for this setting, we have functions to aid in the very important choice of the group size. When individuals come from a heterogeneous population, our group testing regression functions can be used to estimate an individual probability of disease positivity by using the group observations only. We illustrate our functions with data from a multiple vector transfer design experiment and a human infectious disease prevalence study.

Entities:  

Year:  2010        PMID: 21833347      PMCID: PMC3152446     

Source DB:  PubMed          Journal:  R J        ISSN: 2073-4859            Impact factor:   3.984


  6 in total

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Journal:  Biometrics       Date:  2000-12       Impact factor: 2.571

2.  Regression analysis of group testing samples.

Authors:  M Xie
Journal:  Stat Med       Date:  2001-07-15       Impact factor: 2.373

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Journal:  N Engl J Med       Date:  2005-05-05       Impact factor: 91.245

  6 in total
  9 in total

1.  Regression analysis for multiple-disease group testing data.

Authors:  Boan Zhang; Christopher R Bilder; Joshua M Tebbs
Journal:  Stat Med       Date:  2013-05-23       Impact factor: 2.373

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Authors:  Boan Zhang; Christopher R Bilder; Joshua M Tebbs
Journal:  Biom J       Date:  2013-02-08       Impact factor: 2.207

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7.  Determination of Varying Group Sizes for Pooling Procedure.

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Journal:  Comput Math Methods Med       Date:  2019-04-01       Impact factor: 2.238

8.  Group testing via hypergraph factorization applied to COVID-19.

Authors:  David Hong; Rounak Dey; Xihong Lin; Brian Cleary; Edgar Dobriban
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  9 in total

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