Anand N. Vidyashankar

Quality of different in-clinic test systems for feline immunodeficiency virus and feline leukaemia virus infection

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.

Statistical and biological considerations in evaluating drug efficacy in equine strongyle parasites using fecal egg count data

Anthelmintic resistance (AR) is a serious problem for the control of equine gastrointestinal nematodes, particularly in the cyathostomins. The fecal egg count reduction test (FECRT) is the most common method for diagnosing AR and serves as the practical gold standard. However, accurate quantification of resistance and especially accurate diagnosis of emerging resistance to avermectin/milbemycin (A/M) drugs, is hampered by a lack of accepted standards for study design, data analysis, and data interpretation. In order to develop rational evidence-based standards for diagnosis of resistance, one must first take into account the numerous sources of variability, both biological and technical, that affect the measurement of fecal egg counts (FECs). Though usually ignored, these issues can greatly impact the observed efficacy. Thus, to accurately diagnose resistance on the basis of FECRT data, it is important to reduce levels of variability through improved study design, and then deal with inherent variability that cannot be removed, by performing thorough and proper statistical analysis. In this paper we discuss these issues in detail, and provide an explanation of the statistical models and methods that are most appropriate for analyzing these types of data. We also provide several examples using data from laboratory, field, and simulation experiments illustrating the benefits of these approaches.

COVARIATE-ADJUSTED RESPONSE-ADAPTIVE DESIGNS FOR BINARY RESPONSE

An adaptive allocation design for phase III clinical trials that incorporates covariates is described. The allocation scheme maps the covariate-adjusted odds ratio from a logistic regression model onto [0, 1]. Simulations assume that both staggered entry and time to response are random and follow a known probability distribution that can depend on the treatment assigned, the patients response, a covariate, or a time trend. Confidence intervals on the covariate-adjusted odds ratio is slightly anticonservative for the adaptive design under the null hypothesis, but power is similar to equal allocation under various alternatives for n = 200. For similar power, the net savings in terms of expected number of treatment failures is modest, but enough to make this design attractive for certain studies where known covariates are expected to be important and stratification is not desired, and treatment failures have a high ethical cost.

Strongylus vulgaris associated with usage of selective therapy on Danish horse farms—Is it reemerging?

Nematodes belonging to the order Strongylida are ubiquitous in grazing horses, and the large strongyle Strongylus vulgaris is considered the most pathogenic. This parasite was originally described widely prevalent in equine establishments, but decades of frequent anthelmintic treatment appears to have reduced the prevalence dramatically. Increasing levels of anthelmintic resistance in cyathostomin parasites have led to implementation of selective therapy to reduce further development of resistance. It has been hypothesized that S. vulgaris could reoccur under these less intensive treatment circumstances. The aim with the present study was to evaluate the occurrence of S. vulgaris and the possible association with usage of selective therapy. A total of 42 horse farms in Denmark were evaluated for the presence of S. vulgaris using individual larval cultures. Farms were either using a selective therapy principle based on regular fecal egg counts from all horses, or they treated strategically without using fecal egg counts. A total of 662 horses were included in the study. Covariate information at the farm and horse level was collected using a questionnaire. The overall prevalence of S. vulgaris was 12.2% at the individual level and 64.3% at the farm level. Farms using selective therapy had horse and farm prevalences of 15.4% and 83.3%, respectively, while the corresponding results for farms not using selective therapy were 7.7% and 38.9%. These findings were found statistically significant at both the horse and the farm level. Stud farms using selective therapy were especially at risk, and occurrence of S. vulgaris was significantly associated with the most recent deworming occurring more than six months prior. The results suggest that a strict interpretation of the selective therapy regimen can be associated with an increased prevalence of S. vulgaris. This suggests that modifications of the parasite control programs could be considered on the studied farms, but it remains unknown to which extent this can be associated with increased health risks for infected horses.

Local limit theory and large deviations for supercritical Branching processes

In this paper we study several aspects of the growth of a supercritical Galton-Watson process {Z_n:n\ge1}, and bring out some criticality phenomena determined by the Schroder constant. We develop the local limit theory of Z_n, that is, the behavior of P(Z_n=v_n) as v_n\nearrow \infty, and use this to study conditional large deviations of {Y_{Z_n}:n\ge1}, where Y_n satisfies an LDP, particularly of {Z_n^{-1}Z_{n+1}:n\ge1} conditioned on Z_n\ge v_n.

ASYMPTOTIC INFERENCE FOR HIGH-DIMENSIONAL DATA

In this paper, we study inference for high-dimensional data characterized by small sample sizes relative to the dimension of the data. In particular, we provide an infinite-dimensional framework to study statistical models that involve situations in which (i) the number of parameters increase with the sample size (that is, allowed to be random) and (ii) there is a possibility of missing data. Under a variety of tail conditions on the components of the data, we provide precise conditions for the joint consistency of the estimators of the mean. In the process, we clarify and improve some of the recent consistency results that appeared in the literature. An important aspect of the work presented is the development of asymptotic normality results for these models. As a consequence, we construct different test statistics for one-sample and two-sample problems concerning the mean vector and obtain their asymptotic distributions as a corollary of the infinite-dimensional results. Finally, we use these theoretical results to develop an asymptotically justifiable methodology for data analyses. Simulation results presented here describe situations where the methodology can be successfully applied. They also evaluate its robustness under a variety of conditions, some of which are substantially different from the technical conditions. Comparisons to other methods used in the literature are provided. Analyses of real-life data is also included.

Hierarchical model for evaluating pyrantel efficacy against strongyle parasites in horses

Anthelmintic resistance is an increasing challenge for the control of equine parasites. The fecal egg count reduction test (FECRT) is the practical gold standard method for evaluating reduction in anthelmintic efficacy, but the interpretation is complicated due to high levels of variability. A hierarchical statistical model was described for analysis of FECRT data from multiple farms to evaluate the role of biological factors in determining the strongyle efficacy of pyrantel pamoate in a study performed in Denmark. The model was then used to describe two notions of farm efficacy, namely conditional and marginal efficacy. The median of the lower prediction limits was used to describe a robust classification rule. The performance of the methodology was evaluated using Monte Carlo simulations. The field study was performed on 64 Danish horse farms of different breeds. Of 1644 horses, 614 had egg counts ≥ 200 eggs per gram (EPG) and were treated. Individual coprocultures were performed for identification of Strongylus vulgaris from all horses pre-treatment. Thirty-one farms (48.4%) were positive for S. vulgaris, but pyrantel efficacy was unaffected by the presence of this parasite in the statistical model. Further, there were no significant effects of age, gender, or interactions between these, while the pre-treatment egg count was negatively associated with the egg count reduction. The statistical model classified 81.3%, 10.9%, and 7.8% of farms as no signs of resistance (NR), suspect resistance (SR), and resistance (RE), respectively. In comparison, arithmetic calculations classified 68.8%, 17.2%, and 14.1% in the same categories. Using 10,000 simulated data sets, the methodology provided a classification of farms into different efficacy categories with a false discovery of reduced farm efficacy rate equaling 8.74%. In addition, model-classification was unaffected by presence of single outlier horses in a separate simulation study.

RARE EVENT SIMULATION FOR PROCESSES GENERATED VIA STOCHASTIC FIXED POINT EQUATIONS

In a number of applications, particularly in financial and actuarial mathematics, it is of interest to characterize the tail distribution of a random variable

Development of Strongylus vulgaris-specific serum antibodies in naturally infected foals

V

Inference for Quantitation Parameters in Polymerase Chain Reactions via Branching Processes With Random Effects

satisfying the distributional equation