title: Mixed models: getting the best use of parasitological data.
abstract: Statistical analysis of parasitological data provides a powerful method for understanding the biological processes underlying parasite infection. However, robust and reliable analysis of parasitological data from natural and experimental infections is often difficult where: (1) the distribution of parasites between hosts is aggregated; (2) multiple measurements are made on the same individual host in longitudinal studies; or (3) data are from 'noisy' natural systems. Mixed models, which allow multiple error terms, provide an excellent opportunity to overcome these problems, and their application to the analysis of various types of parasitological data are reviewed here.
authors.affiliation: Department of Biological Sciences, University of Stirling, Stirling FK9 4LA, UK. steve.paterson@stir.ac.uk
authors.forename: Steve
authors.initials: S
authors.lastname: Paterson
authors.forename: Joanne
authors.initials: J
authors.lastname: Lello
mesh_terms: D000818:Animals; D006790:Host-Parasite Interactions; D006801:Humans; D015233:Models, Statistical; D010271:Parasites / Q000472:pathogenicity / Q000502:physiology*; D010272:Parasitic Diseases / Q000453:epidemiology / Q000469:parasitology*