  Large scale disease screening is a complicated process in which high costs
must be balanced against pressing public health needs. When the goal is
screening for infectious disease, one approach is group testing in which
samples are initially tested in pools and individual samples are retested only
if the initial pooled test was positive. Intuitively, if the prevalence of
infection is small, this could result in a large reduction of the total number
of tests required. Despite this, the use of group testing in medical studies
has been limited, largely due to skepticism about the impact of pooling on the
accuracy of a given assay. While there is a large body of research addressing
the issue of testing errors in group testing studies, it is customary to assume
that the misclassification parameters are known from an external population
and/or that the values do not change with the group size. Both of these
assumptions are highly questionable for many medical practitioners considering
group testing in their study design. In this article, we explore how the
failure of these assumptions might impact the efficacy of a group testing
design and, consequently, whether group testing is currently feasible for
medical screening. Specifically, we look at how incorrect assumptions about the
sensitivity function at the design stage can lead to poor estimation of a
procedure's overall sensitivity and expected number of tests. Furthermore, if a
validation study is used to estimate the pooled misclassification parameters of
a given assay, we show that the sample sizes required are so large as to be
prohibitive in all but the largest screening programs
