MEMBERS OF BIOSTATISTICS COLLABORATIVE CORE
Katherine Freeman, Dr.P.H., leader of the BCC and Professor of Biomedical Sciences at the Charles E. Schmidt College of Medicine at FAU, received her doctorate in biostatistics from the Columbia School of Public Health. She is an expert in the design and statistical analysis of multicenter randomized clinical trials (RCT) and observational studies. She is proficient in hierarchical modeling, survival analysis including multi-event methods, and systematic reviews and meta-analysis. She has also written or coauthored several successful NIH and foundation supported research grant applications for which she has been the Principal or Co-Investigator. She has coauthored 130 peer-reviewed articles many of which were published in such high impact journals as the NEJM, JAMA, the Lancet, PLOS, BMC and Pediatrics. Dr. Freeman received an unprecedented mentoring award from the Clinical Research Training Program at the Albert Einstein College of Medicine. She has mentored faculty and medical and dental residents, as well as taught research methodology to attorneys, bioengineers and behavioral scientists. Dr. Freeman was a member of an IRB for 14 years, and has Chaired or been a member of many NIH and industry sponsored Data and Safety Monitoring Boards (DSMB). She has also been a reviewer for NIH and PCORI. Her substantive interests encompass public health and health disparities.
Lun-Ching Chang, Ph.D. received his Ph.D. in Biostatistics from the University of Pittsburgh and is currently an Assistant Professor at the Department of Mathematical Sciences. He has both methodologic research expertise and collaboration experience that have prepared him well interact with investigators. His research interests are related to Statistical Genetics, Bioinformatics, Computational Biology and Biostatistics. Dr. Chang has had many collaborations with researchers in different methodologic areas such as clinical trials, computational biology and genetics, that encompass substantive areas of psychiatry (i.e. major depressive disorder (MDD)), and Alzheimer’s disease and aging studies. He has developed a meta-clustering method to combine multiple genomic studies to identify the robust module in MDD studies and also performed the comprehensive evaluation of various meta-analysis methods in applications of genomic studies such as cancers of the prostate, brain, and breast, and for MDD and lung disease. Dr. Chang has served as the principal biostatistician on various collaborative research studies involving molecular biology and genetic characterization of depression, VSNL co-expression networks in aging include Alzheimer's disease pathway, and the role of genetic sex in affect regulation and expression of GABA-related genes across species.
Michael DeDonno, Ph.D. received his M.A. and Ph.D. in psychology from Case Western Reserve University. As a cognitive psychologist, Dr. DeDonno has conducted research with branches of the US military in exploring cognitive factors of Special Forces soldiers. He has also worked with behavioral healthcare hospitals in determining behavioral aspects of individuals with severe mental illness, and has served as a consultant to the behavioral healthcare and pharmaceutical industries. Dr. DeDonno is an Assistant Professor at Florida Atlantic University, where he teaches research methodology and advanced statistics. In 2017, Dr. DeDonno was the recipient of the Florida Atlantic University College of Education, Distinguished Teacher of the Year Award. As a recipient of a National Institute of Health (NIH) award, he has presented research at major psychological and medical conferences. Topics have included healthy aging, techniques that enhance the act of learning, and aspects of psychophysiology. His published work can be found in book chapters, academic, medical, and law journals.
Neela Manage, Ph.D. received her Ph.D. in Economics with specialization in Econometrics from George Washington University. As an Associate Professor of Economics, she has taught econometrics at both the undergraduate and graduate levels (including a required computer lab for applications with real world data involving applications to health), and has directed graduate level theses that involve applications of a variety of statistical methods. Dr. Manage has taught both cross-sectional as well as time-series methods using a wide range of applications. Her research and teaching have included the following topics: discrete-choice (Probit, Logit) and limited dependent variable models (Tobit, Poisson models for count data), time-series methods (stationarity, cointegration, vector error correction and vector autoregressive models, time-varying volatility and ARCH models), panel data models (fixed-effects, random effects), and propensity-score matching estimation of treatment effects. These statistical methods are relevant to many branches of economics, finance, education, engineering, nursing and health sciences, and extend and overlap many biostatistics methodologies.
Will R. McConnell, Ph.D. , Assistant Professor of Sociology, received his doctorate in Sociology and MS in Applied Statistics from Indiana University. His areas of expertise include Social Networks, Aging, Mental Health, Medical Sociology, and quantitative methods. He is proficient in social network data collection & analysis, multilevel modeling, and panel data models, and he is building experience in machine learning methods. One line of his research examines the dynamic relationships between social network context and chronic illness management, particularly in the areas of disability and mental health. A second line of research uses electronic health records to examine how professional networks among physicians influence healthcare delivery. Will's work has been published in leading journals such as the Journal of Health and Social Behavior and Network Science. He currently teaches courses on Research Methods, Aging, and Mental Health, and he mentors several undergraduate and graduate student research projects.
John D. Morris, Ph.D., University of Florida, is Professor Emeritus at Florida Atlantic University, and currently Methodologist in the Department of Educational Leadership and Research Methodology in the College of Education. His research agenda encompasses a wide array of methodological topics largely drawn from multivariate models, with particular interest in prediction methods, psychometric theory, and computer simulation methods. He has published 170 national/international refereed articles and papers, primarily as first author, that examine such topics as the effect of collinearity and potentially resultant validity concentration on the performance of alternative prediction model weighting algorithms, and methods to explore optimizing coefficient alpha. These have been published in such peer-reviewed methodology journals as Psychological Bulletin, Multivariate Behavioral Research, the American Educational Research Journal, Educational and Psychological Measurement, and the Journal of Marketing Research. Dr. Morris has served as Principal Statistician or Co-Investigator on government sponsored research grants. He teaches multivariate statistics, psychometric theory, and facilitates a seminar in research proposal development. Dr. Morris mentors faculty and doctoral students from substantive areas of health, psychology, business and education, to become independent investigators. Additionally, he was awarded the title Educational Researcher of the Year from the Florida Educational Research Association.
David Newman, Ph.D. is an Associate Professor in the Christine E. Lynn College of Nursing, with responsibilities for teaching undergraduate nursing students research design and advanced statistical methods that include discriminate and predictive models; he also serves as the biostatistics mentor for doctoral nursing students. Dr. Newman has served as the Co-I/Statistician on numerous research (e.g., R01s, R15s) and program grant awards including those funded by NIH, HRSA, and PCORI that often involved coordinating and analyzing data from multi-center sites. He has also analyzed data from CMS Medicare databases. Methodologically, Dr. Newman specializes in quantitative and mixed/multimethod research designs that allow for a multidimensional perspective for the development and testing of new instruments. He has completed certification in various statistical programs such as the Microsoft Big Data Analysis course using Hadoop and R to manage and analyze many data structures, often using various psychometric techniques. He is proficient in advanced statistical analytic techniques and quantitative specialties in multiple linear regressions, generalized linear and nonlinear mixed models, path analysis and structural equation modeling, Rasch modeling, and conformity factor analysis.
Necibe Tuncer, Ph.D. is an associate professor in Florida Atlantic University’s Department of Mathematical Sciences. She received her doctorate in mathematics from Auburn University in Alabama As a mathematical epidemiologist, Tuncer specializes in developing multiscale epidemiological models that are linked to an infected host’s immune response. Her work on immuno-epidemiological models establishes a relationship between within-host immune dynamics and infection distribution at the population level. Integrating disease models across several scales requires the development of novel structured models, as well as analytical and computational methods for understanding and applying model dynamics to epidemiological and immunological data. The National Science Foundation funded her groundbreaking work on integrating multiple epidemiological scales with various awards.
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