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Screening tests: Can we get more by doing less?
Author(s) -
Tu Xin M.,
Litvak Eugene,
Pagano Marcello
Publication year - 1994
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780131904
Subject(s) - pooling , estimator , false positive paradox , counterintuitive , computer science , inference , statistics , estimation , human immunodeficiency virus (hiv) , econometrics , sample (material) , statistical inference , true positive rate , statistical hypothesis testing , medicine , mathematics , machine learning , artificial intelligence , economics , philosophy , chemistry , management , epistemology , family medicine , chromatography
Abstract 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.