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dc.contributor.advisorBurger, Divan Aristo
dc.contributor.advisorVan Niekerk, Janet
dc.contributor.advisorSchall, Robert
dc.contributor.authorTeise, Chandre Laverne
dc.date.accessioned2018-11-20T09:06:48Z
dc.date.available2018-11-20T09:06:48Z
dc.date.issued2018-04
dc.identifier.urihttp://hdl.handle.net/11660/9543
dc.description.abstractIn controlled animal trials of ectoparasiticides the e cacy of treatments is esti- mated based on the number of surviving parasites with which experimental animals have been infected. Guidelines for the conduct and analysis of animal trials pub- lished by regulatory authorities require that the e cacy of the test treatment (as determined by the Abbott formula) should be at least 90%, for the test treatment to be declared e cacious. This decision rule, therefore, is simply based on a point estimate of e cacy and does not take into account the precision of the estimate; speci cally, proper statistical inference on the e cacy of the test treatment in question is not required. As a consequence, the Type I error probability of falsely declaring a non-e cacious product to be e cacious can be overin ated. In the proposed research project we investigate the use of appropriate statistical decision rules for the e cacy which control the Type I error at a speci ed low level, say 5%. The statistical model for the data assumes a beta-binomial distribution which can accommodate the binomial overdispersion typically associated with such data. A Bayesian approach for implementing the analysis of ectoparasiticide e cacy data is explored.en_US
dc.language.isoenen_US
dc.publisherUniversity of the Free Stateen_US
dc.subjectEctoparasiticidesen_US
dc.subjectStatistical inferenceen_US
dc.subjectAnimal trialsen_US
dc.subjectDissertation (M.Statistics (Mathematical Statistics and Actuarial Science))--University of the Free State, 2018en_US
dc.titleStatistical inference for ectoparasiticide efficacy in animal trialsen_US
dc.typeDissertationen_US
dc.rights.holderUniversity of the Free Stateen_US


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