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Over-dispersed count data typically pose a challenge to analysis usingstandard statistical methods, particularly when evaluating the efficacy of anintervention through the observed effect on the mean. We outline a novelstatistical method for analysing such data, along with a statistically coherentframework within which the observed efficacy is assigned one of four easilyinterpretable classifications relative to a target efficacy: "adequate","reduced", "borderline" or "inconclusive". We illustrate our approach byanalysing the anthelmintic efficacy of mebendazole using a dataset of eggreduction rates relating to three intestinal parasites from a treatment arm ofa randomised controlled trial involving 91 children on Pemba Island, Tanzania.Numerical validation of the type I error rates of the novel method indicatethat it performs as well as the best existing computationally-simple method,but with the additional advantage of providing valid inference in the case ofan observed efficacy of 100%. The framework and statistical analysis methodpresented also allow the required sample size of a prospective study to bedetermined via simulation. Both the framework and method presented have highpotential utility within medical parasitology, as well as other fields whereover-dispersed count datasets are commonplace. In order to facilitate the useof these methods within the wider medical community, user interfaces for bothstudy planning and analysis of existing datasets are freely provided along withour open-source code via: http://www.fecrt.com/framework

A hypothesis testing framework for the ratio of means of two negative  binomial distributions: classifying the efficacy of anthelmintic treatment  against intestinal parasites