Confidence interval procedures for the probability of disease transmission in Multiple-Vector-Transfer designs

Tebbs, Joshua M.; Bilder, Christopher R.

doi:10.1198/1085711043127

Cited by 37 publications

(38 citation statements)

References 24 publications

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“…For this reason, under simple random sampling, the exact or Pearson CI or the Wilson CI are preferred (Agresti and Coull, 1998;Agresti and Min, 2001;Brown et al, 2001). Even in group testing it has been demonstrated that the best (Tebbs and Bilder, 2004). For this reason, Montesinos-Ló pez, et al (2010) proposed sample sizes for pooled data that guarantee narrow confidence intervals under simple random sampling.…”

Section: Discussionmentioning

confidence: 99%

“…Group testing methods have been used to detect diseases in potential donors (Dodd et al, 2002); to detect drugs (Remlinger et al, 2006); to estimate and detect the prevalence of human (Verstraeten et al, 1998), plant (Tebbs and Bilder, 2004) and animal (Peck, 2006) diseases; to detect and estimate the presence of transgenic plants (Yamamura and Hino, 2007;Hernández-Suárez et al, 2008); and to solve problems in information theory (Wolf, 1985) and even in science fiction (Bilder, 2009). When individuals are not nested within clusters, the issue of the number of pools the sample should have to achieve a certain power or precision for estimating the proportion of interestp has been solved (Yamamura and Hino, 2007;Hernández-Suárez et al, 2008;Montesinos-Ló pez et al, 2010, 2011.…”

Section: Introductionmentioning

confidence: 99%

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Optimal sample sizes for group testing in two-stage sampling

et al. 2014

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Optimal sample sizes under a budget constraint for estimating a proportion in a two-stage sampling process have been derived using individual testing. However, when group testing is used, these optimal sample sizes are not appropriate. In this study, optimal sample sizes at the cluster and individual levels are derived for group testing. First, optimal allocations of clusters and individuals are obtained under the assumption of equal cluster sizes. Second, we obtain the relative efficiency (RE) of unequal versus equal cluster sizes when estimating the average population proportion,p. By multiplying the sample of clusters obtained assuming equal cluster size by the inverse of the RE, we adjust the sample size required in the context of unequal cluster sizes. We also show the adjustments that need to be made to allocate clusters and individuals correctly in order to estimate the required budget and achieve a certain power or precision.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal sample sizes for group testing in two-stage sampling

et al. 2014

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“…Interval estimation procedures for group testing have been examined in several studies (Tebbs and Swallow, 2003;Hepworth, 2004Hepworth, , 2005Tebbs and Bilder, 2004). We propose no change to the established interval estimation procedures.…”

Section: Introductionmentioning

confidence: 98%

Debiased Estimation of Proportions in Group Testing

Hepworth

Watson

2008

Journal of the Royal Statistical Society Series C: Applied Statistics

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In the assessment of disease, estimation of the proportion of infected units in a population can sometimes be facilitated by pooling units into groups for testing. Such group testing was used in a study of virus infection levels in carnation plants grown in glasshouses. In group testing problems, the maximum likelihood estimator is a biased estimator of the population proportion. We investigate the bias of the maximum likelihood estimator when testing groups of different size, using fixed and sequential procedures. The possibility of obtaining all positive groups contributes substantially to the bias. Analytical methods are shown to correct the bias for fixed procedures satisfactorily. For sequential procedures, with their uneven bias patterns, we propose a numerical method of correction which produces an almost unbiased estimator. Copyright (c) 2009 Royal Statistical Society.

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“…Group testing, where individuals are composited into pools to screen for a binary characteristic, has a long history of successful application in areas such as human infectious disease detection, veterinary screening, drug discovery, and insect vector pathogen transmission (Pilcher et al, 2005; Peck,2006; Remlinger et al, 2006; Tebbs and Bilder, 2004). Group testing works well in these settings because the prevalence is usually small and individual specimens (e.g., blood, urine, or cattle ear notches) can be composited without loss of diagnostic test accuracy.…”

Section: Introductionmentioning

confidence: 99%

binGroup: A Package for Group Testing

et al. 2010

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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.

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Confidence interval procedures for the probability of disease transmission in Multiple-Vector-Transfer designs

Cited by 37 publications

References 24 publications

Optimal sample sizes for group testing in two-stage sampling

Optimal sample sizes for group testing in two-stage sampling

Debiased Estimation of Proportions in Group Testing

binGroup: A Package for Group Testing

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