A Simulation‐based Study Comparing A Traditional and An Alternative Design for Studies of Experimentally Induced Intestinal Diseases in Pigs

Abstract

A traditional design for studies of pathogenesis of experimentally induced diseases of the large intestine in individual pigs involves euthanasia and necropsy at scheduled times. An alternative design has been developed enabling the sequential in vivo monitoring of events in the intestine of the individual pig before and during disease. The alternative design is based on repeated endoscopy and biopsy sampling to monitor the course of the disease. The purpose of this study was to compare the statistical properties of the two designs. One hundred replicates of a computer-generated simulation of data were performed. In the simulation, data were generated for individual pigs according to the alternative design, i.e. four sequential observations on the outcome of interest, and the 'traditional' design was created by retaining only one observation per pig. Data from the alternative design were analysed with a generalized linear mixed model, accounting for the clustering of observations within animals and the time-dependence between observations within animals, i.e. observations within animals are interdependent and follow a pattern over time. Data from the 'traditional' design were analysed with an ordinary linear model. The results showed that the alternative design was much more efficient than the traditional and that only one-fourth as many pigs were required to achieve the same precision in estimated effects.

Intervene before leaving: clustered lot quality assurance sampling to monitor vaccination coverage at health district level before the end of a yellow fever and measles vaccination campaign in Sierra Leone in 2009

“…Statistical precision is then recalculated assuming that the true coverage in each cluster would vary from the mean lot coverage according to a binomial distribution with preset standard deviations (SD) [2]. Several authors have explored the applications of dividing the sample in smaller clusters, while applying sequential sampling techniques, such as LQAS, to different fields, from the assessment of global acute malnutrition [10-12] to applications in clinical audit [13], veterinary medicine [14], or agriculture [15,16]. WHO has been piloting C-LQAS with Ministries of Health (MoH) in west and central Africa to monitor coverage while vaccination campaigns are in progress in order to identify areas that need mop-up activities before leaving the field [17].…”