Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation in Group Testing when a Dilution Effect exists

  • Published : 2006.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

In group testing, the test unit consists of a group of individuals and each group is tested to classify units from a population as infected or non-infected or estimate the infection rate. If the test group is infected, one or more individuals in the group are presumed to be infected. It is assumed in group testing that classification of group as positive or negative is without error. But, the possibility of false negatives as a result of dilution effects happens often in practice, specially in many clinical researches. In this paper, dilution effect models in group testing are discussed and estimation methods of infection rate are proposed when a dilution effect exists.

Keywords

References

  1. Chen, C.L. and Swallow, W.H (1995). Sensitivity Analysis of variable-size group testing and its related models. Biometrical Journal, Vol. 37, 173 -181 https://doi.org/10.1002/bimj.4710370207
  2. Cahoon-Young, B., Chandler, A., Livermore, T., Caudino, J., and Benjamin, R. (1989). Sensitivity and specificity of pooled versus individual sera In a HIV-antibody prevalence study. Journal of Clinical Microbiology, Vol. 27, 1893-1895
  3. Dorfman, R (1943). The detection of defective members of large population. Annals of Mathematical Statistics, Vol. 14, 436-440 https://doi.org/10.1214/aoms/1177731363
  4. Gastwirth, J.L. and Harnmick, P.A. (1989). Estimation of the prevalence of a rare disease, preserving the anonymity of the subjects by group testing: application to estimating the prevalence of AIDS antibodies in blood donors. Journal of Statistical Planning and Inference, Vol. 22, 15-27 https://doi.org/10.1016/0378-3758(89)90061-X
  5. Emmanuel, J.C. and Bassett, M.T. (1988). Pooling of sera for HIV testing: An economical method for use in developing countries. Journal of Clinical Pathology, Vol. 41, 582-585 https://doi.org/10.1136/jcp.41.5.582
  6. Hung, M. and Sallow, W.H. (1999). Robustness of Group Testing in the Estimation of Proportions. Biometrics, Vol. 55, 231-237 https://doi.org/10.1111/j.0006-341X.1999.00231.x
  7. Kline, R.L., Brithers, T.A, Brookmeyer, R., Zegar, S., and Quinn, T.C. (1989). Evaluation of HIV seroprevalence in population surveys using pooled sera. Journal of Clinical Microbiology, Vol. 27, 1449-1452
  8. Litvak, E., Tu, X.M., and Pagano, M. (1994). Screening for the presence of a disease by pooling sera samples. Journal of the American Statistical Association, Vol. 89, 424--434 https://doi.org/10.1080/01621459.1994.10476764
  9. Monzon, O.T, Paladin, F.J.E., Dimaandal, E., Balis, A.M., Samson, C., and Mitxhell, S. (1992). Relevance of antibody content and test format in HIV testing of pooled sera. AIDS, Vol. 6, 43-48 https://doi.org/10.1097/00002030-199201000-00005
  10. Swallow, W.H. (1985). Group testing for estimation infection rates and probabilities of diseases transmission. Phytopathology, Vol. 75, 1376 -1381
  11. Thompson, K.H. (1962). Estimation of the proportion of vectors in a natural population of insects. Biometrics, Vol. 18, 568-578 https://doi.org/10.2307/2527902
  12. Tu, X.M., Litvak, E., and Pagano, M. (1995). On the informativeness and accuracy of pooled testing in estimating prevalence of a rare disease: Application to HIV screening. Biometrika, Vol. 82, 287-297 https://doi.org/10.1093/biomet/82.2.287