Doctor of Philosophy, Case Western Reserve University, 2022, Clinical Translational Science

The translation of medical therapies from basic and preclinical research to efficacious human interventions is challenging. The majority of candidate therapies fail in early-stage human trials, after showing promise in preclinical work. The primary aim of the research presented herein is to explore the potential role that poor statistical practice in preclinical animal trials might play in contributing to translational failure. First, a comprehensive appraisal of current statistical practice in one area of preclinical neuroscience research was carried out. A close review of the current related literature is presented, and the appraisal includes a tutorial to explain how certain statistical mistakes might result in overly optimistic results, as well as practical recommendations for improvement. A majority of articles included in this appraisal failed to account for sources of non-independence in the data (74-93%) and/or did not analytically account for mid-treatment animal attrition (78%). Ordinal variables were often treated as continuous (37%), outliers were predominantly not mentioned (83%), and plots often concealed the distribution of the data (51%). Next, a sample including both successful and failed human trials for neurologic targets was identified, and rates of statistical mistakes in the associated preceding rodent trials were compared. Failed human trials were found to have higher rates of select sources of potential statistical bias in preceding rodent trials, compared to successful trials. This research provides evidence that a contributing factor to translational failure is statistical misapplication in preclinical animal research in the neurosciences. It provides the groundwork for future research that will provide practical solutions to translational researchers and funders, facilitating preclinical experimental validity to increase the translational success rate.

Committee: Mary Dolansky PhD RN FAAN (Committee Chair); Kenneth Baker PhD (Committee Member); Nancy Obuchowski PhD (Committee Member); Jill Barnholtz-Sloan PhD (Advisor) Subjects: Animal Sciences; Biostatistics; Neurosciences; Statistics

Doctor of Philosophy, The Ohio State University, 2014, Public Health

West Nile virus (WNV) disease in humans causes systemic febrile illness, meningoencephalitis, and death. The WNV, a reemerging pathogen, found its way to New York City, United States of America (U.S.), from the Mediterranean region in Europe in 1999 causing a countrywide epizootic and epidemic by 2003, and by 2012, leaving a reported 37,088 total human cases, 16,196 neuroinvasive cases, 1474 deaths, with an average case fatality rate of 10% in its wake. From 2002-2006, Ohio reported 669 human cases, 487 neuroinvasive cases, and 47 deaths, 5536 WNV positive mosquito pools, and 1328 WNV total positive dead birds. This study used captured data from this time frame to address the gap in translational research between mosquito control theoreticians and practitioners for better understanding, preventing, and controlling WNV transmission hazards and risks to humans, by developing a practical predictive model to be used in their mosquito control programs. Time-delayed indices were constructed as time periods relative to the week mosquitoes were trapped (weeks before and during the trapping week) to reach this goal. Temperature (T), weekly cumulative precipitation (CP), and the Palmer Index (PDI) informed these indices that estimated the temporal position of phases of the mosquito life cycle and the ecological conditions necessary for the development within these phases in relation to the trapping week. Descriptive statistical tools were used to characterize temporal and spatial patterns of: 1) T, CP, and the PDI relative to documented WNV mosquito infection; and, 2) reported human WNV disease, WNV positive bird deaths, mosquito infection rates (IRs), and mosquito density (abundance), by week, year and Ohio County, and within the broader context of the U.S.. Regression analyses were performed using these same indices as predictor variables with mosquito IRs as the outcome to determine the biological and meteorological drivers underlying WNV infections in mosquitoes, an (open full item for complete abstract)

Committee: Michael Bisesi PhD (Committee Chair) Subjects: Environmental Health