Cost analysis in choosing group size when group testing for Potato virus Y in the presence of classification errors

Liu, S.C.; Chiang, Kuo-Szu; Lin, Ching-Sheng; Chung, W.C.; Lin, S.H.; Yang, T.C.

doi:10.1111/j.1744-7348.2011.00513.x

Cited by 21 publications

(9 citation statements)

References 28 publications

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“…Individuals within negative testing groups are declared negative. Because of its simplicity, Dorfman's protocol is the most widely adopted protocol for case identification, and its applications include screening blood donations (Stramer et al, 2004), chlamydia testing (Mund et al, 2008), and potato virus detection (Liu et al, 2011). Because specimens are retested, ω ik is no longer the same as given in Eq.…”

Section: Dorfmanmentioning

confidence: 99%

Group testing regression model estimation when case identification is a goal

2013

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Group testing is frequently used to reduce the costs of screening a large number of individuals for infectious diseases or other binary characteristics in small prevalence situations. In many applications, the goals include both identifying individuals as positive or negative and estimating the probability of positivity. The identification aspect leads to additional tests being performed, known as “retests,” beyond those performed for initial groups of individuals. In this paper, we investigate how regression models can be fit to estimate the probability of positivity while also incorporating the extra information from these retests. We present simulation evidence showing that significant gains in efficiency occur by incorporating retesting information, and we further examine which testing protocols are the most efficient to use. Our investigations also demonstrate that some group testing protocols can actually lead to more efficient estimates than individual testing when diagnostic tests are imperfect. The proposed methods are applied retrospectively to chlamydia screening data from the Infertility Prevention Project. We demonstrate that significant cost savings could occur through the use of particular group testing protocols.

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Section: Dorfmanmentioning

confidence: 99%

Group testing regression model estimation when case identification is a goal

2013

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“…A beta‐distribution is commonly used for a proportion. A beta‐distribution, defining the distribution of a random variable on the closed unit interval [0, 1], can be made very flexible by choosing different shape parameters (α and β) based on empirical data (Gelman et al ., ; Carlin & Louis ; Liu et al ., ). Moreover, in a previous study on disease severity estimation, Hartung & Piepho () simulated the disease severity according to a right (positively) skewed beta‐distribution to obtain specimens for every class in order to investigate whether ordinal rating scales were better compared with percent ratings.…”

Section: Methodsmentioning

confidence: 97%

A discussion on disease severity index values. Part I: warning on inherent errors and suggestions to maximise accuracy

Chiang

Liu

Bock³

2017

Annals of Applied Biology

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135

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A special type of ordinal scale comprising a number of intervals of known numeric ranges can be used when estimating severity of a plant disease. The interval ranges are most often based on the percent area with symptoms [e.g. the Horsfall-Barratt (H-B) scale]. Studies in plant pathology and plant breeding often use this type of ordinal scale. The disease severity is estimated by a rater as a value on the scale and has been used to determine a disease severity index (DSI) on a percentage basis, where DSI (%) = [sum (class frequency × score of rating class)]/[(total number of plants) × (maximal disease index)] × 100. However, very few studies have investigated the effects of different scales on accuracy of the DSI. Therefore, the objectives of this study were to investigate the process of calculating a DSI on a percentage basis from ordinal scale data, and to use simulation approaches to explore the effect of using different methods for calculation of the interval range and the nature of the ordinal scales used on the DSI estimates (%). We found that the DSI is particularly prone to overestimation when using the above formula if the midpoint values of the rating class are not considered. Moreover, the results of the simulation studies show that, if rater estimates are unbiased, compared with other methods tested in this study, the most accurate method for estimation of a DSI is to use the midpoint of the severity range for each class with an amended 10% ordinal scale (an ordinal scale based on a 10% linear scale emphasising severities ≤50% disease, with additional grades at low severities). As for biased conditions, the accuracy for calculating DSI estimates (%) will depend mainly on the degree and direction of the rater bias relative to the actual mean value.

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“…Furthermore, we selected three different variance parameter settings (representing situations of large, medium, and small sample variation) for the fixed means to illustrate the effects of the variance between estimates with the different assessment methods (Figure 1). variable on the closed unit interval 0-1 is commonly used for a proportion and can be made very flexible by choosing different shape parameters (α and β) [35][36][37]. In this study, the means (α/(α + β)) of the random variables of the beta-distributions (given the two chosen parameters: α and β) were set at 1, 5, 10, 20, 30, and 40% severities to demonstrate a range of possible "actual" mean disease severities (%).…”

Section: Simulation Methodsmentioning

confidence: 99%

Effects of Quantitative Ordinal Scale Design on the Accuracy of Estimates of Mean Disease Severity

et al. 2019

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Estimates of plant disease severity are crucial to various practical and research-related needs in agriculture. Ordinal scales are used for categorizing severity into ordered classes. Certain characteristics of quantitative ordinal scale design may affect the accuracy of the specimen estimates and, consequently, affect the accuracy of the resulting mean disease severity for the sample. The aim of this study was to compare mean estimates based on various quantitative ordinal scale designs to the nearest percent estimates, and to investigate the effect of the number of classes in an ordinal scale on the accuracy of that mean. A simulation method was employed. The criterion for comparison was the mean squared error of the mean disease severity for each of the different scale designs used. The results indicate that scales with seven or more classes are preferable when actual mean disease severities of 50% or less are involved. Moreover, use of an amended 10% quantitative ordinal scale with additional classes at low severities resulted in a more accurate mean severity compared to most other scale designs at most mean disease severities. To further verify the simulation results, estimates of mean severity of pear scab on samples of leaves from orchards in Taiwan demonstrated similar results. These observations contribute to the development of plant disease assessment scales to improve the accuracy of estimates of mean disease severities.

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Cost analysis in choosing group size when group testing for Potato virus Y in the presence of classification errors

Cited by 21 publications

References 28 publications

Group testing regression model estimation when case identification is a goal

Group testing regression model estimation when case identification is a goal

A discussion on disease severity index values. Part I: warning on inherent errors and suggestions to maximise accuracy

Effects of Quantitative Ordinal Scale Design on the Accuracy of Estimates of Mean Disease Severity

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