Abstract
We review frequentist and Bayesian approaches for estimating animal-level disease prevalence using pooled samples obtained by simple random sampling. We determine the preferred approach for different prevalence scenarios and with varying knowledge about sensitivity and specificity values. When sensitivity and specificity are perfect or known, we can choose between the large-sample theory estimates and the one-to-one relationship exact estimates. When sensitivity and specificity are unknown, we must use large-sample theory estimates or Bayesian methodology (which gives exact estimates). However, when the large-sample theory produces a negative lower confidence limit, we must use one of the exact methods. We compare estimates from each approach using culture results from pools of 20 eggs from three flocks on a California ranch that were producing eggs that were contaminated with Salmonella enteritidis phage type 4.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 211-225 |
| Number of pages | 15 |
| Journal | Preventive Veterinary Medicine |
| Volume | 39 |
| Issue number | 3 |
| DOIs | |
| State | Published - Apr 9 1999 |
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Keywords
- Bayesian methods
- Pooled testing
- Prevalence
- Salmonella enteritidis
- Sensitivity
- Specificity
ASJC Scopus subject areas
- Food Animals
- Animal Science and Zoology
Cite this
Comparison of methods for estimation of individual-level prevalence based on pooled samples. / Cowling, David W.; Gardner, Ian; Johnson, Wesley O.
In: Preventive Veterinary Medicine, Vol. 39, No. 3, 09.04.1999, p. 211-225.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Comparison of methods for estimation of individual-level prevalence based on pooled samples
AU - Cowling, David W.
AU - Gardner, Ian
AU - Johnson, Wesley O.
PY - 1999/4/9
Y1 - 1999/4/9
N2 - We review frequentist and Bayesian approaches for estimating animal-level disease prevalence using pooled samples obtained by simple random sampling. We determine the preferred approach for different prevalence scenarios and with varying knowledge about sensitivity and specificity values. When sensitivity and specificity are perfect or known, we can choose between the large-sample theory estimates and the one-to-one relationship exact estimates. When sensitivity and specificity are unknown, we must use large-sample theory estimates or Bayesian methodology (which gives exact estimates). However, when the large-sample theory produces a negative lower confidence limit, we must use one of the exact methods. We compare estimates from each approach using culture results from pools of 20 eggs from three flocks on a California ranch that were producing eggs that were contaminated with Salmonella enteritidis phage type 4.
AB - We review frequentist and Bayesian approaches for estimating animal-level disease prevalence using pooled samples obtained by simple random sampling. We determine the preferred approach for different prevalence scenarios and with varying knowledge about sensitivity and specificity values. When sensitivity and specificity are perfect or known, we can choose between the large-sample theory estimates and the one-to-one relationship exact estimates. When sensitivity and specificity are unknown, we must use large-sample theory estimates or Bayesian methodology (which gives exact estimates). However, when the large-sample theory produces a negative lower confidence limit, we must use one of the exact methods. We compare estimates from each approach using culture results from pools of 20 eggs from three flocks on a California ranch that were producing eggs that were contaminated with Salmonella enteritidis phage type 4.
KW - Bayesian methods
KW - Pooled testing
KW - Prevalence
KW - Salmonella enteritidis
KW - Sensitivity
KW - Specificity
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UR - http://www.scopus.com/inward/citedby.url?scp=0033537985&partnerID=8YFLogxK
U2 - 10.1016/S0167-5877(98)00131-7
DO - 10.1016/S0167-5877(98)00131-7
M3 - Article
VL - 39
SP - 211
EP - 225
JO - Preventive Veterinary Medicine
JF - Preventive Veterinary Medicine
SN - 0167-5877
IS - 3
ER -