No abstract

No abstract

Results indicated that ocular and adnexal SCCs treated with adjuvant radiation therapy had a significantly lower recurrence rate, compared with SCCs treated without adjuvant radiation therapy, independent of anatomic location.

The prevalence of resistance found in this study was higher than that reported previously, suggesting that anthelmintic resistance in equine cyathostomes is becoming a major problem. Furthermore, data from these 5 southern states, which are geographically and physiographically distinct, were remarkably similar. This suggests that drug resistance in cyathostomes is highly prevalent throughout the entire southern United States and probably nationwide.

No abstract

Many new diagnostic in-house tests for identification of feline immunodeficiency virus (FIV) and feline leukaemia virus (FeLV) infection have been licensed for use in veterinary practice, and the question of the relative merits of these kits has prompted comparative studies. This study was designed to define the strengths and weaknesses of seven FIV and eight FeLV tests that are commercially available. In this study, 536 serum samples from randomly selected cats were tested. Those samples reacting FIV-positive in at least one of the tests were confirmed by Western blot, and those reacting FeLV-positive were confirmed by virus isolation. In addition, a random selection of samples testing negative in all test systems was re-tested by Western blot (100 samples) and by virus isolation (81 samples). Specificity, sensitivity, positive and negative predictive values of each test and the quality of the results were compared.

No abstract

This paper develops a methodology for robust Bayesian inference through the use of disparities. Metrics such as Hellinger distance and negative exponential disparity have a long history in robust estimation in frequentist inference. We demonstrate that an equivalent robustification may be made in Bayesian inference by substituting an appropriately scaled disparity for the log likelihood to which standard Monte Carlo Markov Chain methods may be applied. A particularly appealing property of minimum-disparity methods is that while they yield robustness with a breakdown point of 1/2, the resulting parameter estimates are also efficient when the posited probabilistic model is correct. We demonstrate that a similar property holds for disparity-based Bayesian inference. We further show that in the Bayesian setting, it is also possible to extend these methods to robustify regression models, random effects distributions and other hierarchical models. The methods are demonstrated on real world data.