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In the estimation of a proportion p by group testing (pooled testing), retesting of units within positive groups has received little attention due to the minimal gain in precision compared to testing additional units. If acquisition of additional units is impractical or too expensive, and testing is not destructive, we show that retesting can be a useful option. We propose the retesting of a random grouping of units from positive groups, and compare it with nested halving procedures suggested by others. We develop an estimator of p for our proposed method, and examine its variance properties. Using simulation we compare retesting methods across a range of group testing situations, and show that for most realistic scenarios, our method is more efficient. [ABSTRACT FROM AUTHOR]

Group testing is the process of combining individual samples and testing them as a group for the presence of an attribute. The use of such testing to estimate proportions is an important statistical tool in many applications. When samples are collected and tested in groups of different size, complications arise in the construction of exact confidence intervals. In this case, the numbers of positive groups has a multivariate distribution, and the difficulty stems from a lack of a natural ordering of the sample points. Exact two-sided intervals such as the equal-tail method based on maximum likelihood estimation, and those based on joint probability or likelihood ratio statistics, have been previously considered. In this paper several new estimators are developed and assessed. We show that the combined tails (or Blaker) method based on a suitable ordering statistic, is the best choice in this setting. The methods are illustrated using a study involving the infection prevalence of Myxobolus cerebralis among free-ranging fish. [ABSTRACT FROM AUTHOR]

Site occupancy, as estimated by the probability of presence, is used for monitoring species populations. However, the detection of species at individual sites is often subject to errors. In order to accurately estimate occupancy we must simultaneously account for imperfect detectability by estimating the probability of detection. The problem with estimating occupancy arises from not knowing whether a nondetection occurred at an occupied site due to imperfect detectability (sampling zeros), or the nondetection resulting from an unoccupied site (fixed zeros). We evaluated the performance of the basic, normal approximation, studentised and percentile methods for approximating confidence limits for occupancy and detection of species. Using coverage and average interval width, we demonstrated that the studentised estimator was generally superior to the others, except when a small sample of sites are selected. Under this circumstance and when calculating limits for detection, no estimator produced reliable results. The experimental factors we considered include: (i) number of sites; (ii) number of survey occasions; (iii) probabilities of presence (occupancy) and detection; and (iv) overdispersion in the capture matrix. Similar conclusions were reached both for the simulated studies and a case study. Overall, estimation near the boundaries of the probability of occupancy and detectability was difficult. [ABSTRACT FROM AUTHOR]

Group testing has been used in many fields of study to estimate proportions. When groups are of different size, the derivation of exact confidence intervals is complicated by the lack of a unique ordering of the event space. An exact interval estimation method is described here, in which outcomes are ordered according to a likelihood ratio statistic. The method is compared with another exact method, in which outcomes are ordered by their associated MLE. Plots of theP-value against the proportion are useful in examining the properties of the methods. Coverage provided by the intervals is assessed using several realistic grouptesting procedures. The method based on the likelihood ratio, with a mid-Pcorrection, is shown to give very good coverage in terms of closeness to the nominal level, and is recommended for this type of problem. [ABSTRACT FROM AUTHOR]