Bayesian statistics for parasitologists.
Bayesian statistical methods are increasingly being used in the analysis of parasitological data. Here, the basis of differences between the Bayesian method and the classical or frequentist approach to statistical inference is explained. This is illustrated with practical implications of Bayesian analyses using prevalence estimation of strongyloidiasis and onchocerciasis as two relevant examples. The strongyloidiasis example addresses the problem of parasitological diagnosis in the absence of a gold standard, whereas the onchocerciasis case focuses on the identification of villages warranting priority mass ivermectin treatment. The advantages and challenges faced by users of the Bayesian approach are also discussed and the readers pointed to further directions for a more in-depth exploration of the issues raised. We advocate collaboration between parasitologists and Bayesian statisticians as a fruitful and rewarding venture for advancing applied research in parasite epidemiology and the control of parasitic infections.
Basáñez
María-Gloria
MG
Department of Infectious Disease Epidemiology, Faculty of Medicine (St Mary's Campus), Imperial College London, Norfolk Place, W2 1PG, London, UK. m.basanez@imperial.ac.uk
Marshall
Clare
C
Carabin
Hélène
H
Gyorkos
Theresa
T
Joseph
Lawrence
L
D000818:Animals; D000871:Anthelmintics / Q000627:therapeutic use*; D001499:Bayes Theorem; D016021:Epidemiologic Studies; D006801:Humans; D007559:Ivermectin / Q000627:therapeutic use; D009855:Onchocerciasis / Q000188:drug therapy* / Q000453:epidemiology / Q000517:prevention & control; D010274:Parasitology / Q000379:methods / Q000706:statistics & numerical data*; D015995:Prevalence; D013322:Strongyloidiasis / Q000453:epidemiology*
D000871:Anthelmintics; D007559:Ivermectin