Details
| Original language | English |
|---|---|
| Pages (from-to) | 56-60 |
| Number of pages | 5 |
| Journal | R Journal |
| Volume | 2 |
| Issue number | 2 |
| Publication status | Published - 2010 |
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.
ASJC Scopus subject areas
- Mathematics(all)
- Statistics and Probability
- Mathematics(all)
- Numerical Analysis
- Decision Sciences(all)
- Statistics, Probability and Uncertainty
Sustainable Development Goals
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In: R Journal, Vol. 2, No. 2, 2010, p. 56-60.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - BinGroup
T2 - A package for group testing
AU - Bilder, Christopher R.
AU - Zhang, Boan
AU - Schaarschmidt, Frank
AU - Tebbs, Joshua M.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84874988850&partnerID=8YFLogxK
U2 - 10.32614/rj-2010-016
DO - 10.32614/rj-2010-016
M3 - Article
AN - SCOPUS:84874988850
VL - 2
SP - 56
EP - 60
JO - R Journal
JF - R Journal
SN - 2073-4859
IS - 2
ER -