MAARC: Data and Analytics Support Core
Data and Analytics Support Core (DASC)
Within the MAARC, the Data and Analytics Support Core (DASC) plays a key role by providing fundamental data and analytic services to all CRCs through five aims. These aims are establishing rigorous standards for JCOIN-funded trials and data collection activities, creating a secure computing infrastructure, providing comprehensive data collection and management resources to JCOIN CRCs, providing technical assistance to individual CRCs, and implementing a wholistic approach to JCOIN resource sharing.
- Aim 1: Establish rigorous standards for the design, conduct and analysis of all trials and other data collection activities funded by JCOIN to ensure that the data collected: (1) are of the greatest scientific value; (2) are analyzed in appropriate ways using advanced statistical and other analytic methods, as necessary; (3) may be combined across different studies and with external data sources to increase power, permit replication, and answer new research questions; and (4) may be shared with other researchers while protecting participant confidentiality.
- Aim 2: Create a secure, scalable computing infrastructure based on the proven, open-source Gen3 Data Commons platform to facilitate: (1) compiling and archiving harmonized data from the CRCs and other sources using common data elements (CDEs) from NIH’s repository, whenever possible; (2) performing meta-analyses using data from multiple sites; (3) collaborative analyses using shared data, software and/or analytic methods; and (4) access to data within the JCOIN network and by the research community.
- Aim 3: Provide integrated, comprehensive resources to individual JCOIN CRCs for data collection and management including: (1) identifying and designing instruments for data collection including cost and quality of life measures, using the PhenX toolkit whenever possible; (2) software tools for data collection, including access to REDCap (including mobile interface) and to the newly-developed Computerized Adaptive Test-Substance Abuse (CAT-SA); (3) software tools and workflows for data cleaning, QC and management; and (4) procedures for protecting participant confidentiality including an honest broker service.
- Aim 4: Provide technical assistance to individual CRCs in a variety of analytic methods including: (1) advanced statistical methods for the design and analysis of randomized, controlled trials as well as meta-analyses of multiple studies; (2) methods for the coding and analysis of structured, qualitative data; and (3) advanced computational methods for predictive analytics and analysis of spatial and social network data.
- Aim 5: In coordination with the Administrative Core, design and implement a wholistic approach to broad sharing of JCOIN resources: (1) create and maintain a public website for distribution of information about CRC studies and datasets available; (2) extend the Data Commons to include public-facing tools for exploring available datasets at an aggregate level; (3) develop and implement a governance model for the Data Commons which permits researchers to request and access data while maintaining appropriate safeguards to protect participant confidentiality; and (4) conduct training in using data and computational resources for members of the network and screencasts for public distribution.
Lead – UC
Robert Grossman is the Frederick H. Rawson Professor, a Professor of Medicine and Computer Science, and the Jim and Karen Frank Director of the Center for Data Intensive Science (CDIS) at the University of Chicago. Since 2011, he has been the Chief Research Informatics Officer (CRIO) of the Biological Sciences Division, and, since 2016, he has been the Co-Chief of the Section of Computational Biomedicine and Biomedical Data Science in the Department of Medicine at the University of Chicago. He is the Chair of the not-for-profit Open Commons Consortium, which develops and operates clouds to support research in science, medicine, health care, and the environment. He has been a Partner of Analytic Strategy Partners LLC since 2017. He was the founder and Managing Partner of Open Data Group from 2002-2016. Open Data Group provides analytic services and products so that companies can deploy predictive models. Learn more.
Co-lead – UC
Dr. Donald Hedeker’s main area of expertise is in the development and use of advanced statistical methods for clustered and longitudinal data, with particular emphasis on mixed-effects models. He is the primary author of four freeware computer programs for mixed-effects analysis: MIXREG for normal-theory models, MIXOR for dichotomous and ordinal outcomes, MIXNO for nominal outcomes, and MIXPREG for counts. Learn more.
Dr. Ricky N. Bluthenthal is the Associate Dean for Social Justice and a Professor in the Department of Preventive Medicine and the Institute for Prevention Research at the Keck School of Medicine, University of Southern California. He received a BA in History and Sociology from the University of California Santa Cruz and a PhD in sociology from the University of California Berkeley. His research has established the effectiveness of syringe exchange programs, tested novel interventions and strategies to reduce HIV risk and improve HIV testing among injection drug users and men who has sex with men, documented how community conditions contribute to health disparities, and examined health policy implementation. His current studies include a randomized controlled trial to test the efficacy of a single session intervention to reduce injection initiation risk behaviors among established people who inject drugs and an observational epidemiological study to examine if increased cannabis availability results to decreased opioid use among people who inject drugs. Learn more.
Dr. Robert Gibbons is a statistician interested in the areas of biostatistics, environmental statistics, and psychometrics. Major themes in his work include development of linear and nonlinear mixed effects regression models for analysis of longitudinal data, analysis of environmental monitoring data and inter-laboratory calibration, item response theory and computerized adaptive testing, and the development of new statistical methods in pharmacoepidemiology and drug safety. He is an elected member of the Institute of Medicine of the National Academy of Sciences and a fellow of the American Statistical Association. Gibbons has coauthored many publications, including “Full-Information Item Bi-Factor Analysis,” “Waiting for Organ Transplantation,” and “Weighted Random-Effects Regression Models with Application to Inter-Laboratory Calibration.” He earned his PhD in statistics and psychometrics from the University of Chicago and his BA in chemistry and mathematics from the University of Denver. Learn more.