Origins and Applications of Pooling Sample Clauses

Origins and Applications of Pooling. The introduction, or rather, popularization of pooling is often attributed to Xxxxxx Xxxx- man (1943), who applied the technique to blood samples when testing soldiers for syphilis. The idea behind this type of pooling is that, when a disease has low prevalence, it is more cost effective to test pooled groups of specimens, then retest each of the specimens from any positive pools individually, rather than to simply perform lab tests on each individual specimen. Since then, pooling has become a popular strategy for reducing cost, for instance, in testing donated blood for HIV or determining regional prevalence of blood-bourne dis- eases (Xxxxxxxxxx, 1999; Xxxxxxxx et al., 1988; Xxx, Xxxxx, and Xxx, 1993; Vansteelandt, Xxxxxxxxxxx, and Xxxxxxxxxxx, 0000). Xxxxxxxx and Xxxxxx (1999) note that pooling can help preserve irreplaceable speci- mens, by requiring only a portion of the stored sample for analysis. Along the same lines, pooling can make use of samples that may lack enough volume to be analyzed individu- ally, thus extracting information from specimens that may otherwise have been presumed unusable. Pooling can also be helpful from the standpoint of reducing the number of assay non- detects when a laboratory limit of detection is present. Xxxxxxxxxxx and Xxxxxx (2008) discuss the advantages to pooling when estimating mean and variance of biospecimens subject to a limit of detection, where the utility of pooling depends on the value of the detection threshold relative to the mean of the biomarker value. While pooling may be performed for various reasons, in this paper we assume that the primary motivating factor is to reduce laboratory costs. Study designs recommended in this paper are made under this assumption, and additional consideration of alternate designs may be necessary if pooling is done for a different reason, such as to reduce the number of non-detects when a limit of detection is present. Specifically, we focus our investigation on efficiently estimating regression coefficients when a continuous outcome is subject to pooling. Let Yij denote the jth subject in the ith pool, and let xij be the vector of covariates corresponding to this outcome. Furthermore, suppose that we can model the relationship between the predictor and outcome with one of the following models:
Sample 1
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