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Meta-analysis techniques applied in prevalence rate estimation
Discussiones Mathematicae. Probability and Statistics, Tome 33 (2013) no. 1-2, pp. 79-97.

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In some cases, the estimators obtained in compound tests have better features than the traditional ones, obtained from individual tests, cf. Sobel and Elashoff (1975), Garner et al. (1989) and Loyer (1983). The bias, the efficiency and the robustness of these estimators are investigated in several papers, e.g. Chen and Swallow (1990), Hung and Swallow (1999) and Lancaster and Keller-McNulty (1998). Thus, the use of estimators based on compound tests not only allows a substantial saving of costs, but they also can (in some situations) be more accurate than the estimators based on the individual tests.
Mots-clés : compound tests, estimation of prevalence, meta-analysis, sensitivity, specificity 
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Martins, João; Felgueiras, Miguel; Santos, Rui. Meta-analysis techniques applied in prevalence rate estimation. Discussiones Mathematicae. Probability and Statistics, Tome 33 (2013) no. 1-2, pp. 79-97. https://geodesic-test.mathdoc.fr/item/DMPS_2013_33_1-2_a5/

[1] F. Anscombe, The transformation of poisson, binomial and negative-binomial data, Biometrika 35 (1948) 246-254.

[2] T. Berger, J.W. Mandell and P. Subrahmanya, Maximally efficient two-stage screening, Biometrics 56 (2000) 833-840. doi: 10.1111/j.0006-341X.2000.00833.x.

[3] B.J. Biggerstaff and R.L. Tweedie, Incorporating Variability in Estimates of Heterogeneity in the Random Effects Model in Meta-analysis, Stat. Med. 16 (1997) 753-768. doi: 10.1002/(SICI)1097-0258(19970415)16:7753::AID-SIM494>3.0.CO;2-G.

[4] C.R. Bilder, B. Zang, F. Schaarschmidt and J.M. Tebbs, binGroup: a package for group testing, The R Journal 2 (2010) 56-60.

[5] C.L. Chen and W.H. Swallow, Using group testing to estimate a proportion, and to test the binomial model, Biometrics 46 (1990) 1035-1046. doi: 10.2307/2532446.

[6] W.G. Cochran, The combination of estimates from different experiments, Biometrics 10 (1954) 101-129. doi: 10.2307/3001666.

[7] R. DerSimonian and N. Laird, Meta-analysis in clinical trials, Control. Clin. Trials 7 (1986) 177-178. doi: 10.1016/0197-2456(86)90046-2.

[8] R. Dorfman, The detection of defective members in large populations, Ann. Math. Statistics 14 (1943) 436-440. doi: 10.1214/aoms/1177731363.

[9] H.M. Finuncan, The blood testing problem, Appl. Stat. 13 (1964) 43-50. doi: 10.2307/2985222.

[10] F.C. Garner, M.A. Stapanian, E.A. Yfantis and L.R. Williams, Probability Estimation With Sample Compositing Techniques, Journal of Official Statistics 5 (1989) 365-374.

[11] J. Hartung, G. Knapp and B.K. Sinha, Statistical Meta-Analysis with Applications (John Wiley Sons, Hoboken, 2008). doi: 10.1002/9780470386347.

[12] P. Holland, Covariance stabilizing transformations, Ann. Stat. 14 (1973) 84-92. doi: 10.1214/aos/1193342384.

[13] J.M. Hughes-Oliver, Pooling experiments for blood screening and drug discovery, in: Screening - Methods for Experimentation in Industry, Drug Discovery, and Genetics, Dean and Lewis (Ed(s)), (New York: Springer, 2006) 48-68.

[14] M. Hung and W.H. Swallow, Robustness of Group Testing in the Estimation of Proportions, Biometrics 55 (1999) 231-237. doi: 10.1111/j.0006-341X.1999.00231.x.

[15] F.K. Hwang, Group testing with a dilution effect, Biometrika 63 (1976) 671-673. doi: 10.1093/biomet/63.3.671.

[16] N. Johnson, S. Kotz and X. Wu, Inspection Errors for Attributes in Quality Control (Chapman and Hall Ltd., NewYork, 1991).

[17] N. Johnson, S. Kotz and N. Balakrishnan, Continuous Univariate Distributions, Vol. 2 (John Wiley Sons, NewYork, 1993).

[18] H. Kim, M. Hudgens, J. Dreyfuss, D. Westreich and C. Pilcher, Comparison of group testing algorithms for case identification in the presence of testing errors, Biometrics 63 (2007) 1152-1163. doi: 10.1111/j.1541-0420.2007.00817.x.

[19] E. Kulinskaya, S. Morgenthaler and R.G. Staudte, Meta Analysis: a guide to calibrating and combining statistical evidence (Wiley, Chichester, 2008).

[20] V.A. Lancaster and S. Keller-McNulty, A Review of Composite Sampling Methods, JASA 93 (1998) 1216-1230. doi: 10.1080/01621459.1998.10473781.

[21] S.C. Liu, K.S. Chiang, C.H. Lin, W.C. Chung, S.H. Lin and T.C. Yang, Cost analysis in choosing group size when group testing for Potato virus Y in the presence of classification errors, Ann. Appl. Biol. 159 (2011) 491-502. doi: 10.1111/j.1744-7348.2011.00513.x.

[22] M.W. Loyer, Bad probability, good statistics, and group testing for binomial estimation, Am. Stat. 37 (1983) 57-59. doi: 10.1080/00031305.1983.10483091.

[23] A.L. Rukhin, B.J. Biggerstaff and M.G. Vangel, Restricted maximum likelihood estimation of a common mean and the Mandel-Paul algorithm, J. Stat. Plan. Infer. 83 (2000) 319-330. doi: 10.1016/S0378-3758(99)00098-1.

[24] R. Santos, D. Pestana and J.P. Martins, Extensions of Dorfman's theory, in: Selected Papers of SPE 2010, Portuguese Statistical Society (Ed(s)), (New York: Springer, 2012) in print.

[25] K.M. Sobel and R.M. Elashoff, Group testing with a new goal, estimation, Biometrika 62 (1975) 181-193. doi: 10.1093/biomet/62.1.181.

[26] A. Sterret, On the detection of defective members of large populations, Ann. Math. Statistics 28 (1957) 1033-1036. doi: 10.1214/aoms/1177706807.

[27] L.M. Wein and S.A. Zenios, Pooled testing for HIV screening: capturing the dilution effect, Oper. Res. 44 (1996) 543-569. doi: 10.1287/opre.44.4.543.

[28] M. Xie, K. Tatsuoka, J. Sacks and S.S. Young, Group testing with blockers and synergism, JASA 96 (2001) 92-102. doi: 10.1198/016214501750333009.

[29] S.A. Zenios and L.M. Wein, Pooled testing for HIV prevalence estimation exploiting the dilution effect, Stat. Med. 17 (1998) 1447-1467. doi: 10.1002/(SICI)1097-0258(19980715)17:131447::AID-SIM862>3.0.CO;2-K.