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An Overview of Quantitative Continuous Compound Analysis

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Dynamics, Games and Science

Part of the book series: CIM Series in Mathematical Sciences ((CIMSMS,volume 1))

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Abstract

The application of compound tests in clinical analysis or acceptance sampling exults in resource savings. Furthermore, quantitative compound tests allow to infer whether the amount of some substance of any individual in the group is greater or lower than a prefixed threshold. However, the use of this type of tests must be done with caution to avoid having a high probability of misclassification. This work uses the weight of the tails of the underlying distribution as a measure of the adequacy of the application of continuous compounds tests.

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References

  1. Berger, T., Mandell, J.W., Subrahmanya, P.: Maximally efficient two-stage screening. Biometrics 56, 833–840 (2000)

    Article  MATH  Google Scholar 

  2. Bilder, C.R., Zang, B., Schaarschmidt, F., Tebbs, J.M.: Bingroup: a package for group testing. R J. 2, 56–60 (2010)

    Google Scholar 

  3. Boswell, M.T., Gore, S.D., Lovison, G., Patil, G.P.: Annotated bibliography of composite sampling, Part A: 1936–1992. Environ. Ecol. Stat. 3, 1–50 (1996)

    Article  Google Scholar 

  4. Chen, C., Swallow, W.: Sensitivity analysis of variable-sized group testing and its related continuous models. Biometrical J. 37, 173–181 (1995)

    Article  MATH  Google Scholar 

  5. Dorfman, R.: The detection of defective members in large populations. Ann. Math. Stat. 14, 436–440 (1943)

    Article  Google Scholar 

  6. Finucan, H.M.: The blood testing problem. Appl. Stat. J. Roy. St. C 13, 43–50 (1964)

    Google Scholar 

  7. Garner, F.C., Stapanian, M.A., Yfantis, E.A., Williams, L.R.: Probability estimation with sample compositing techniques. J. Off. Stat. 5, 365–374 (1989)

    Google Scholar 

  8. Gastwirth, J.L., Johnson, W.O.: Screening with cost-effective quality control: potential applications to HIV and drug testing. JASA 89, 972–981 (1994)

    Article  MATH  Google Scholar 

  9. Gastwirth, J.L.: The efficiency of pooling in the detection of rare mutations. Am. J. Hum. Genet. 67, 1036–1039 (2000)

    Article  Google Scholar 

  10. Gill, A., Gottlieb, D.: The identification of a set by successive intersections. In: Information and Control, 20–25. Ellis Horwood, Chichester (1974)

    Google Scholar 

  11. Hung, M., Swallow, W.: Robustness of group testing in the estimation of proportions. Biometrics 55, 231–237 (1999)

    Article  MATH  Google Scholar 

  12. Hoaglin, D.M., Mosteller, F., Tukey, J.W.: Understanding Robust and Exploratory Data Analysis. Wiley, New York (1983)

    MATH  Google Scholar 

  13. Hughes-Oliver, J.M.: Pooling experiments for blood screening and drug discovery. In: Dean, A., Lewis, S. (eds.) Screening: Methods for Experimentation in Industry, Drug Discovery, and Genetics, 48–68. Springer, Berlin (2006)

    Google Scholar 

  14. Hwang, F.K.: Group testing with a dilution effect. Biometrika 63, 671–673 (1976)

    Article  MATH  Google Scholar 

  15. Kim, H., Hudgens, M., Dreyfuss, J., Westreich, D., Pilcher, C.: Comparison of group testing algorithms for case identification in the presence of testing errors. Biometrics 63, 1152–1163 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  16. Johnson, N.L., Kotz, S., Wu, X.: Inspection Errors for Attributes in Quality Control. Chapman and Hall, New York (1991)

    Book  Google Scholar 

  17. Lancaster, V.A., Keller-McNulty, S.: A review of composite sampling methods. J. Am. Stat. Assoc. 93, 1216–1230 (1998)

    Article  Google Scholar 

  18. Litvak, E., Tu, X.M., Pagano, M.: Screening for the presence of a disease by pooling sera samples. J. Am. Stat. Assoc. 89, 424–434 (1994)

    Article  MATH  Google Scholar 

  19. Liu, S.C., Chiang, K.S., Lin, C.H., Chung, W.C., Lin, S.H., Yang, T.C.: Cost analysis in choosing group size when group testing for potato virus Y in the presence of classification errors. Ann. Appl. Biol. 159, 491–502 (2011)

    Article  Google Scholar 

  20. Loyer, M.W.: Bad probability, good statistics, and group testing for binomial estimation. Am. Stat. 37, 57–59 (1983)

    Google Scholar 

  21. Martins, J.P., Santos, R., Sousa, R.: Testing the maximum by the mean in quantitative group tests. In: Pacheco, A., et al. (eds.) New Advances in Statistical Modeling and Applications, Studies in Theoretical and Applied Statistics, Selected Papers of the Statistical Societies. Springer, 55–63 (2014)

    Chapter  Google Scholar 

  22. Phatarfod, R.M., Sudbury, A.: The use of a square array scheme in blood testing. Stat. Med. 13, 2337–2343 (1994)

    Article  Google Scholar 

  23. Roederer, M., Koup, R.A.: Optimized determination of T cell epitope responses. J. Immunol. Methods 274, 221–228 (2003)

    Article  Google Scholar 

  24. Santos, R., Pestana, D., Martins, J.P.: Extensions of Dorfman’s theory. In: Oliveira, P.E., et al. (eds.) Studies in Theoretical and Applied Statistics, Recent Developments in Modeling and Applications in Statistics, 179–189. Springer, New York (2013)

    Google Scholar 

  25. Santos, R., Felgueiras, M., Martins, J.P.: Known mean, unknown maxima? Testing the maximum knowing only the mean. Commun. Stat. Simul. Comput. (Published online: 23 Jan 2014)

    Google Scholar 

  26. Santos, R., Martins, J.P., Felgueiras, M.: Discrete compound tests and Dorfman’s methodology in the presence of misclassification. In: Kitsos, C., et al. (eds.) Theory and Practice of Risk Assessment, Springer Proceedings in Mathematics & Statistics 136, Springer (2015)

    Google Scholar 

  27. Sobel, M., Elashoff, R.: Group testing with a new goal, estimation. Biometrika 62, 181–193 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  28. Sobel, M., Groll, P.A.: Group testing to eliminate efficiently all defectives in a binomial sample. Bell Syst. Tech. J. 38, 1179–1252 (1959)

    Article  MathSciNet  Google Scholar 

  29. Sterret, A.: On the detection of defective members of large populations. Ann. Math. Stat. 28, 1033–1036 (1957)

    Article  Google Scholar 

  30. Tu, X.M., Litvak, E., Pagano, M.: Studies of AIDS and HIV surveillance, screening tests: can we get more by doing less? Stat. Med. 13, 1905–1919 (1994)

    Article  Google Scholar 

  31. Tu, X.M., Litvak, E., Pagano, M.: On the informativeness and accuracy of pooled testing in estimating prevalence of a rare disease: Application to HIV screening. Biometrika 82(2), 287–297 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  32. Wein, L.M., Zenios, S.A.: Pooled testing for HIV screening: capturing the dilution effect. Oper. Res. 44, 543–569 (1996)

    Article  MATH  Google Scholar 

  33. Woodbury, C.P., Fitzloff, J.F., Vincent, S.S.: Sample multiplexing for greater throughput in HPLC and related methods. Anal. Chem. 67, 885–890 (1995)

    Article  Google Scholar 

  34. Zenios, S., Wein, L.: Pooled testing for HIV prevalence estimation exploiting the dilution effect. Stat. Med. 17, 1447–1467 (1998)

    Article  Google Scholar 

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Acknowledgements

Research partially sponsored by national funds through the Fundação Nacional para a Ciência e Tecnologia, Portugal—FCT under the project PEst-OE/MAT/UI0006/2014.

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Correspondence to Rui Santos .

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Santos, R., Martins, J.P., Felgueiras, M. (2015). An Overview of Quantitative Continuous Compound Analysis. In: Bourguignon, JP., Jeltsch, R., Pinto, A., Viana, M. (eds) Dynamics, Games and Science. CIM Series in Mathematical Sciences, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-16118-1_35

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