Review: analysis of parasite and other skewed counts


Alexander, N; (2012) Review: analysis of parasite and other skewed counts. Tropical medicine & international health, 17 (6). pp. 684-693. ISSN 1360-2276 DOI: https://doi.org/10.1111/j.1365-3156.2012.02987.x

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Abstract

Objective To review methods for the statistical analysis of parasite and other skewed count data. Methods Statistical methods for skewed count data are described and compared, with reference to a 10-year period of Tropical Medicine and International Health (TMIH). Two parasitological datasets are used for illustration. Results The review of TMIH found 90 articles, of which 89 used descriptive methods and 60 used inferential analysis. A lack of clarity is noted in identifying the measures of location, in particular the Williams and geometric means. The different measures are compared, emphasising the legitimacy of the arithmetic mean for the skewed data. In the published articles, the t test and related methods were often used on untransformed data, which is likely to be invalid. Several approaches to inferential analysis are described, emphasising (1) non-parametric methods, while noting that they are not simply comparisons of medians, and (2) generalised linear modelling, in particular with the negative binomial distribution. Additional methods, such as the bootstrap, with potential for greater use are described. Conclusions Clarity is recommended when describing transformations and measures of location. It is suggested that non-parametric methods and generalised linear models are likely to be sufficient for most analyses.

Item Type: Article
Keywords: statistical data analysis, parasitology, statistics, non-parametric, regression analysis, analyse statistique des donnees, parasitologie, statistiques, non parametrique, analyse de regression, analisis, estadistico de datos, Parasitologia, Estadistica, No parametrico, Analisis de regresion, geometric means, t-test, infection, poisson, regression, intensity, community, malaria, impact, model
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology
Research Centre: Tropical Epidemiology Group
Web of Science ID: 304316100002
URI: http://researchonline.lshtm.ac.uk/id/eprint/37843

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