Characterizing populations of individuals using pooled samples

Biomonitoring involves the assessment of human or animal populations by measuring organic or biological compounds or their metabolites in the body fluids or tissues of individuals in those populations. Pooling samples before making analytical measurements can reduce the costs of biomonitoring by reducing the number of analyses. By proper choice of pooled-sample design, population means can be estimated without measuring individual samples. I present a statistical method for characterizing an entire population distribution of such compounds by exploiting the theoretic relationship between interindividual-sample variance and the variation between pooled samples. I use simulation experiments to determine an optimum pooled-sample design as a function of the number of subpopulations and the number of available samples. Using pooled samples to characterize populations is not only more cost-efficient, but also in some cases it can lead to more precise and less biased parameter estimation than that occurs with individual samples.