%0 Journal Article
%T Optimal pooling strategies for laboratory testing
%A Brian G. Williams
%J Quantitative Biology
%D 2010
%I arXiv
%X We consider the optimal strategy for laboratory testing of biological samples when we wish to know the results for each sample rather than the average prevalence of positive samples. If the proportion of positive samples is low considerable resources may be devoted to testing samples most of which are negative. An attractive strategy is to pool samples. If the pooled samples test positive one must then test the individual samples, otherwise they can all be assumed to be negative. The pool should be big enough to reduce the number of tests but not so big that the pooled samples are almost all positive. We show that if the prevalence of positive samples is greater than 30% it is never worth pooling. From 30% down to 1% pools of size 4 are close to optimal. Below 1% substantial gains can be made by pooling, especially if the samples are pooled twice. However, with large pools the sensitivity of the test will fall correspondingly and this must be taken into consideration. We derive simple expressions for the optimal pool size and for the corresponding proportion of samples tested.
%U http://arxiv.org/abs/1007.4903v1