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Sample size under inverse negative binomial group testing for accuracy in parameter estimation.

ABSTRACT: BACKGROUND: The group testing method has been proposed for the detection and estimation of genetically modified plants (adventitious presence of unwanted transgenic plants, AP). For binary response variables (presence or absence), group testing is efficient when the prevalence is low, so that estimation, detection, and sample size methods have been developed under the binomial model. However, when the event is rare (low prevalence <0.1), and testing occurs sequentially, inverse (negative) binomial pooled sampling may be preferred. METHODOLOGY/PRINCIPAL FINDINGS: This research proposes three sample size procedures (two computational and one analytic) for estimating prevalence using group testing under inverse (negative) binomial sampling. These methods provide the required number of positive pools ([Formula: see text]), given a pool size (k), for estimating the proportion of AP plants using the Dorfman model and inverse (negative) binomial sampling. We give real and simulated examples to show how to apply these methods and the proposed sample-size formula. The Monte Carlo method was used to study the coverage and level of assurance achieved by the proposed sample sizes. An R program to create other scenarios is given in Appendix S2. CONCLUSIONS: The three methods ensure precision in the estimated proportion of AP because they guarantee that the width (W) of the confidence interval (CI) will be equal to, or narrower than, the desired width ([Formula: see text]), with a probability of [Formula: see text]. With the Monte Carlo study we found that the computational Wald procedure (method 2) produces the more precise sample size (with coverage and assurance levels very close to nominal values) and that the samples size based on the Clopper-Pearson CI (method 1) is conservative (overestimates the sample size); the analytic Wald sample size method we developed (method 3) sometimes underestimated the optimum number of pools.

SUBMITTER: Montesinos-Lopez OA

PROVIDER: S-EPMC3310835 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Sample size under inverse negative binomial group testing for accuracy in parameter estimation.

Montesinos-López Osval Antonio OA Montesinos-López Abelardo A Crossa José J Eskridge Kent K

PloS one 20120322 3

<h4>Background</h4>The group testing method has been proposed for the detection and estimation of genetically modified plants (adventitious presence of unwanted transgenic plants, AP). For binary response variables (presence or absence), group testing is efficient when the prevalence is low, so that estimation, detection, and sample size methods have been developed under the binomial model. However, when the event is rare (low prevalence <0.1), and testing occurs sequentially, inverse (negative) ...[more]

PMID: 22457714

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Project description:Estimation of age-at-death from human skeletal remains is fundamental in forensic anthropology as part of the construction of the biological profile of the individual under study. At the same time, skeletal age-at-death estimation in adults is problematic due to the disparity between chronological and biological age, the important inter-individual variability at the rate of skeletal aging, and inherent biases in the available methodologies (e.g., age mimicry). A recent paper proposed a method for skeletal age-at-death estimation based on multiple anatomical traits and machine learning. A software was also created, DRNNAGE, for the easy implementation of this method. The authors of that study supported that their methods have very high repeatability and reproducibility, and the mean absolute error of the age estimation was ~6 years across the entire adult age span, which is particularly high and promising. This paper tests the proposed methodology on a modern documented Greek sample of 219 adult individuals from the Athens Collection, with age-at-death from 19 to 99 years old. The sample was split into males and females as well as into individuals under and over 50 years old. We also divided the sample in 10-year intervals. First, intra- and inter-observer error was estimated in order to assess repeatability and reproducibility of the variables employed for age-at-death estimation. Then, the validity (correct classification performance) of DRNNAGE for each anatomical region individually, as well as all combined, was evaluated on each demographic separately and on the pooled sample. According to the results, some of the variables showed very low repeatability and reproducibility, thus their use should be cautious. The DRNNAGE software showed overall highly accurate age-at-death estimates for individuals older than 50 years, but poor on younger adults, with only exception the cranial sutures, which performed surprisingly well for all age groups. Overall, these results support the importance of cross-validation and the use of population-specific methods in forensic anthropology.

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Sample size under inverse negative binomial group testing for accuracy in parameter estimation.

Publications

Sample size under inverse negative binomial group testing for accuracy in parameter estimation.

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