The Data Management and Analysis Center (DMAC) provides the infrastructure, expertise, and support of data and statistics to researchers within the Department of Family and Preventive Medicine as well as other research groups at RUSH. The DMAC is comprised of faculty members, data analysts and data managers. The Center is housed within the department and provides operational support for studies in addition to data management and statistical analysis. With a combined experience of over 30 years, DMAC staff has considerable experience collaborating at every stage of the research process from grant submissions to the conduct of the studies to final manuscript preparations.
A notable strength of the DMAC is the development and use of methods for the effective design and analysis of randomized clinical trials involving behavioral interventions. In the past 15 years, the DMAC has supported and is currently supporting numerous large-scale behavioral trials. Other areas of expertise include, but not limited to, database design, sample size calculations, experimental design, longitudinal data analysis (including intensive longitudinal data), categorical data analysis, survival analysis, and mediation analysis. Software skills include SnapSurveys, REDCap, MS SQL, MS Access, SAS, R, SPSS, Stata, MPlus, Python and SAP Crystal Reports.
As a service to the scientific community, DMAC accepts requests from researchers to use data from past studies and trials. The list of available datasets along with a brief study description can be found here. Requests are accepted through the REDCap survey link.
Meet the DMAC staff
Professor
Director, Data Management and Analysis Center
Sumihiro Suzuki, PhD is a classically trained mathematical statistician with expertise in biostatistics, sequential analysis, and applications of novel statistical methods to public health. His recent methodological research has focused on sequentially planned procedures for clinical trials design, change point detection to identify the onset of flu seasons, and natural language processing for surveillance of probationers absconding supervision. In collaborative research, he has served as the biostatistician in numerous study areas including COPD, HIV, cancer, infectious diseases, and substance abuse. These efforts have led to over 60 peer-reviewed publications and funding support from the National Cancer Institute, National Institute for Minority Health and Health Disparities, National Institute of Allergy and Infectious Diseases, and the Centers for Disease Control and Prevention.
Professor
Imke Janssen, PhD, combines expertise in biostatistics, epidemiology and behavioral clinical trial methodology to inform her research and to guide and mentor junior researchers to frame their questions and identify appropriate methods to answer them. She has served as the biostatistician on five NIH-funded behavioral clinical trials, primarily targeting underserved populations.
She was the principal investigator of an innovative study of chronic stress and subclinical cardiovascular disease in women. She has over 85 publications, many of which focus on psychosocial risk for preclinical cardio-metabolic diseases in women. Currently she is leading the Chicago site of the SWAN study, a longitudinal observational study of midlife women now entering its 26th year. This landmark study has shaped our understanding of women’s health through midlife into early old age.
Assistant Director, Data Management and Analysis Center
As a biostatistician with several years of experience, Kelly Karavolos, MA, has an extended knowledge and familiarity with a variety of basic as well as more complex statistical concepts and methodologies. Examples are t-test, correlation, linear, logistic and Poisson regression, ANOVA, factor analysis, survival analysis and non-parametric techniques. She has worked with longitudinal data conducting repeated measures analyses and randomized clinical trials data using hierarchical linear models.
Kelly has experience working with multidisciplinary teams investigating pathways to address public health issues. She collaborates with investigators to develop statistical analysis plans for grants and provide analytic support for scientific manuscripts and presentations. She continually tries to identify novel strategies and statistical techniques to address complex research questions in health-related subject areas and the burden of chronic disease.
Senior Data Analyst
Tami Olinger is the Senior Data Manager for the DMAC. Her career expertise includes the design, build, and maintenance of electronic data capture and transfer systems, data storage and security, as well as study operations management. Her vast work includes everything from small pilot studies to large multisite national trials utilizing multiple platforms for data management, allowing collaboration with every level of study personnel.
Statistician 2
As a biostatistician and data manager in DMAC, Yeh's professional journey has been marked by a deep commitment to data analysis and research. With a background in biostatistics, her role involved developing statistical analysis plan and applying advanced statistical methodologies, including linear models, logistic regression, Poisson regression, longitudinal data analysis, and survival analysis, to address research questions effectively using statistical software such as SAS and R. Additionally, one of Yeh's areas of expertise is in utilizing REDCap, a versatile tool for data collection and management, which she has leveraged to streamline research projects and ensure data accuracy.
Statistician 2
Elizabeth Avery has worked supporting first the data management needs for the department and then the statistical needs for the department’s research for the last 20+ years. She has worked to support research teams from the grant develop stages, through implementation, analysis of data and publication of the results. Study designs have included randomized trials (e.g., clustered, behavioral), intervention pilot studies, cross sectional surveys, CMS Utilization studies, program evaluation and longitudinal observation studies. Avery’s analysis areas include study quality control investigation, t-test, correlation, cross sectional regression (linear, logistic and Poisson regression), longitudinal analysis, factor analysis, survival analysis and non-parametric techniques. Avery has experience working with multidisciplinary teams including medical practitioners, researchers, data managers, biostatistics, study staff and community members. She works in partnership with these study teams to develop tailored data and analytic solutions.
Statistician 2
Joshua Longcoy is a dedicated statistician with a strong commitment to public health research. His expertise in statistical analysis, particularly in multilevel modeling with community-level data and individual Electronic Medical Record (EMR) data, has impacted nursing and community health. He is currently working with the Rush BMO Institute for Health Equity on projects geared towards advancing health equity, which is a critical and significant area of focus in public health. Joshua’s work reflects the intersection of statistical expertise and public health, where data-driven insights can drive meaningful change and improvements in healthcare and community well-being.
Senior Data Coordinator
As a Senior Data Coordinator, Paul Glover specializes in database management, data governance, and data manipulation. Glover graduated Summa Cum Laude from the University at Buffalo, with a major in Psychology. He acquired various certifications in SQL/Python and utilizes those skills to construct data queries, clean raw data, and organize reports. In his growing experience in clinical trials, he has used data capture programs like SNAP and REDCap and structured that data using MS Excel, MS Access, and Crystal Reports.
Glover has partnered with Scientists, Biostatisticians, and Data Analysts on multiple studies to develop calculated strategies for obtaining, managing, and presenting analysis concerning public health initiatives. Examples include multiple-arm analysis, repeated-measure study designs, and longitudinal data supervision. He hopes to continue to grow his knowledge and experience in research to be better equipped to offer novel and vital contributions to the group effort of enhancing society.