Elizabeth Juarez-Colunga, PhD
Dr. Juarez-Colunga is an assistant professor in the Department of Biostatistics and Informatics in the Colorado School of Public Health. She received her BS in Applied Mathematics and MSc in Statistics in Mexico, and her doctoral degree in Statistics from Simon Fraser University in Canada. Elizabeth’s areas of expertise and interest include: (i) analysis of data with dependencies at different levels including longitudinal and clustered data, which builds upon generalized linear and non linear mixed models, (ii) analysis of repeated events data such as asthma exacerbations, which evolve as extension of survival analysis methods and non normal outcome methods, (iii) joint modeling of multiple outcomes, and (iv) analysis of observational data.
Contact Info: Elizabeth.Juarez-Colunga@ucdenver.edu
L. Miriam Dickinson, PhD
Dr. Miriam Dickinson is a professor in the Department of Family Medicine with a secondary appointment in the Department of Biostatistics and Informatics at University of Colorado Denver. She received her BA and PhD (biostatistics) from Tulane University, MS in biometrics from University of Arkansas for Medical Science, and completed an NIMH-sponsored fellowship in mental health services research. Miriam’s expertise and interests include: (i) design and analysis of cluster randomized pragmatic trials, (ii) multilevel and longitudinal modeling, (iii) design and analytic methods for practice-based and community trials, including covariate constrained randomization methods.
Dr. Diane Fairclough is a professor in the Department of Biostatistics and Informatics. Diane received her doctoral degree in Biostatistics from the University of North Carolina and has held appointments at St. Jude Children’s Research Hospital, Harvard School of Public Health, AMC Cancer Research Center and the University of Colorado Denver. She is a past President of the International Society for Quality of Life Research and has over 200 peer-reviewed publications. Dr. Fairclough's primary research interest is Quality of Life, outcomes in palliative/hospice care, and psychosocial sequelae of cancer and its therapy in pediatric and adult patients. Dr. Fairclough’s statistical research interests include the design and analysis of longitudinal studies with non-random missing data due to disease morbidity or mortality. She is the author of Design and Analysis of Quality of Life Studies in Clinical Trials, 2nd edition (2010).
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John Rice, PhD
Assistant Professor, Department of Biostatistics and Informatics
Dr. John Rice is an assistant professor in the Department of
Biostatistics and Informatics in the Colorado School of Public Health. He
received his MSPH in Biostatistics from Emory University in 2010, and his PhD
in Biostatistics from the University of Michigan in 2015, where his
dissertation focused on statistical methods for cancer research. He completed
postdoctoral training at the University of Rochester in 2017, where he worked
in the areas of HIV testing behavior and cardiovascular outcomes, prior to
joining the faculty at UC Denver. His research interests include longitudinal
data analysis, recurrent events, and semiparametric regression methods for
binary and semicontinuous outcomes data.
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Leader, ACCORDS Analytical Core
Ms. Beaty manages the efforts of the ACCORDS analytic team as well as performing data analysis on a number of projects. She has been a SAS Certified Base Programmer since 2003 and has been involved with SAS Users Groups at the local, regional and national levels. She works with projects involving both pediatric and adult health outcomes.
Contact Info: firstname.lastname@example.org
Epidemiologist, Co-Leader ACCORDS Analytical Core
Michelle is a Research Instructor in the Department of Pediatrics at the University of Colorado Denver School of Medicine. She received her Master in Public Health in Hospital and Molecular Epidemiology from the University of Michigan and her PhD in Epidemiology from the University of North Carolina Chapel Hill. She co-supervises the data analyst team with Brenda Beaty.
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Our team also includes 12 data analysts who are MS and PhD trained data analysts. They have experience with many different biostatistical methods and data sources.