000			03433nab a22004097a 4500
001			G96879
003			MX-TxCIM
005			20240919020947.0
008			220505s2012 xxu|||p|op||| 00| 0 eng d
022	0		_a1932-6203
024	8		_ahttps://doi.org/10.1371/journal.pone.0032250
040			_aMX-TxCIM
041			_aeng
090			_aCIS-6750
100	1		_aMontesinos-Lopez, O.A. _8I1706800 _92700 _gGenetic Resources Program
245	1	0	_aSample size under inverse negative binomial group testing for accuracy in parameter estimation
260			_aSan Francisco, CA (USA) : _bPublic Library of Science, _c2012.
500			_aPeer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=1932-6203
500			_aPeer review
500			_aOpen Access
520			_aBackground. 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 (), 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 (), with a probability of . 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.
536			_aGenetic Resources Program
546			_aText in English
591			_aCIMMYT Informa No. 1805
594			_aCCJL01
595			_aCSC
650		7	_93763 _aStatistical data _2AGROVOC
650		7	_98831 _aGenetic engineering _2AGROVOC
650		7	_95934 _aGene pools _2AGROVOC
650		7	_98832 _aMonte Carlo method _2AGROVOC
700	1		_92702 _aMontesinos-Lopez, A.
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aEskridge, K. _92704
773	0		_tPLoS ONE _gv. 7, no. 3, p. e32250 _dSan Francisco, CA (USA) : Public Library of Science, 2012. _wG94957 _x1932-6203
856	4		_uhttp://hdl.handle.net/10883/2243 _yOpen Access through DSpace
942			_cJA _2ddc _n0
999			_c29306 _d29306