Studies of AIDS and HIV surveillance. Screening tests: can we get more by doing less?
Estimating the prevalence of the human immunodeficiency virus (HIV) in a group is challenging; this is especially so when the prevalence is small. One reason is that the presence of measurement errors resulting from the limited precision of tests makes estimation, using traditional methods, impossible in some screening situations. Measurement error is real, ignoring it leads to severe bias, and inference about the prevalence becomes unsatisfactory. Indeed, in a low prevalence situation the expected number of false positives is very high, often even higher than the number of true positives. The second reason is that in the low prevalence areas the large sample is needed in order to obtain non-zero estimate. This is usually a very costly, and often unrealistic, solution. This paper considers the advantages and disadvantages of pooled testing as an alternative solution to this problem. We show that by pooling sera samples we not only achieve a cost saving but also, which is counterintuitive, an increase in the estimation accuracy. We also discuss the statistical issues associated with the resulting estimator.
Department of Mathematics and Statistics, University of Pittsburgh, PA 15240.
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D000163:Acquired Immunodeficiency Syndrome / Q000175:diagnosis / Q000453:epidemiology*; D001800:Blood Specimen Collection / Q000706:statistics & numerical data*; D016001:Confidence Intervals; D004797:Enzyme-Linked Immunosorbent Assay / Q000379:methods / Q000706:statistics & numerical data; D015658:HIV Infections / Q000175:diagnosis / Q000453:epidemiology*; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D015995:Prevalence; D018401:Sample Size; D015983:Selection Bias; D012680:Sensitivity and Specificity