Estimation of the prevalence of a rare disease, preserving the anonymity of the subjects by group testing: application to estimating the prevalence of aids antibodies in blood donors

Gastwirth, Joseph L.; Hammick, Patricia A.

doi:10.1016/0378-3758(89)90061-x

Cited by 96 publications

(89 citation statements)

References 10 publications

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“…The results in Section 4 also demonstrate that the approximation is reliable for more typical pool sizes. It is worth emphasizing that (12) uses only the first two moments of > (y) and \ (y) and eliminates the need to perform the numerical integration in (8). However, we expect that the estimate obtained using approximation (12), which we will call the proposed estimate, will differ from the true maximum likelihood estimate (MLE).…”

Section: Two-moment Approximationmentioning

confidence: 94%

“…We refer to these procedures as the exact parametric procedure and the proposed parametric procedure, and their main difference lies in the way they approximate the densities f IK 6 (x) in equation (8). The exact procedure approximates f IK 6 (x) using a very precise look-up table representation obtained from a Monte Carlo simulation.…”

Section: Parametric Proceduresmentioning

confidence: 99%

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Pooled testing for HIV prevalence estimation: exploiting the dilution effect

Zenios

Wein²

1998

Statist. Med.

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We study pooled (or group) testing as a method for estimating the prevalence of HIV; rather than testing each sample individually, this method combines various samples into a pool and then tests the pool. Existing pooled testing procedures estimate the prevalence using dichotomous test outcomes. However, HIV test outcomes are inherently continuous, and their dichotomization may eliminate useful information. To overcome this problem, we develop a parametric procedure that utilizes the continuous outcomes. This procedure employs a hierarchical pooling model and estimates the prevalence using the likelihood equation. The likelihood equation is solved using an iterative algorithm, and a simulation study shows that our procedure yields very accurate estimates at a fraction of the cost of existing procedures.

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Section: Two-moment Approximationmentioning

confidence: 94%

Section: Parametric Proceduresmentioning

confidence: 99%

Pooled testing for HIV prevalence estimation: exploiting the dilution effect

Zenios

Wein²

1998

Statist. Med.

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“…Using the randomization method of Gastwirth and Hammick (1989), Kline et al (1989), and Hung and Swallow (2000), and using the observed proportion positive as an estimate of the true p i , we randomly assign individuals to groups within strata, thereby simulating group observations of size s ¼ 5 and s ¼ 10. The resulting numbers of tests also appear in Table 4.…”

Section: Applicationmentioning

confidence: 99%

More Powerful Likelihood Ratio Tests for Isotonic Binomial Proportions

Tebbs

Swallow

2003

Biometrical J

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SummaryBinomial group testing involves pooling individuals into groups and observing a binary response on each group. Results from the group tests can then be used to draw inference about population proportions. Its use as an experimental design has received much attention in recent years, especially in public-health screening experiments and vector-transfer designs in plant pathology. We investigate the benefits of group testing in situations wherein one desires to test whether or not probabilities are increasingly ordered across the levels of an observed qualitative covariate, i.e., across strata of a population or among treatment levels. We use a known likelihood ratio test for individual testing, but extend its use to group-testing situations to show the increases in power conferred by using group testing when operating in this constrained parameter space. We apply our methods to data from an HIV study involving male subjects classified as intraveneous drug users.

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“…This strategy and its variations developed later, often referred to as group testing or pooled testing, have received substantial attention for efficient identification of an event or estimation of the probability that the event occurs; see Sobel & Groll (1959), Sobel & Elashoff (1975), Le (1981), Gastwirth & Hammick (1989), Chen & Swallow (1990), Farrington (1992), Gastwirth & Johnson (1994), Hughes-Oliver & Swallow (1994), Litvak et al (1994), Tu et al (1995), Barcellos et al (1997), Brookmeyer (1999), Hung & Swallow (1999), Hughes-Oliver & Rosenberger (2000) and Tebbs & Swallow (2003). An attractive feature of group testing is that retesting on individuals is not necessary if one is only interested in estimation of the probability of a positive test.…”

Section: Introductionmentioning

confidence: 99%