  Counting the number of parasite eggs in faecal samples is a widely used
diagnostic method to evaluate parasite burden. Typically a sub-sample of the
diluted faeces is examined for eggs. The resulting egg counts are multiplied by
a specific correction factor to estimate the mean parasite burden. To detect
anthelmintic resistance, the mean parasite burden from treated and untreated
animals are compared. However, this standard method has some limitations. In
particular, the analysis of repeated samples may produce quite variable results
as the sampling variability due to the counting technique is ignored. We
propose a hierarchical model that takes this sampling variability as well as
between-animal variation into account. Bayesian inference is done via Markov
chain Monte Carlo sampling. The performance of the hierarchical model is
illustrated by a re-analysis of faecal egg count data from a Swedish study
assessing the anthelmintic resistance of nematode parasite in sheep. A
simulation study shows that the hierarchical model provides better
classification of anthelmintic resistance compared to the standard method.
