Methods Core | Center for Translational Behavioral Science

Principle Investigator: Sylvie Naar, PhD
- Project Director: Sara Green, MSW; Project Manager & Evaluator: Brenda Echeverri, MS; Project Coordinator: Samantha Chahin, MPH
Specific Aims:
- To successfully recruit and train 100 Fellows dedicated to behavioral intervention develop for cancer prevention/treatment and related health behaviors over the 4-year grant period, with substantial minority representation (25 Fellows per year).
- To increase the skills of Fellows in a phased approach, with associated innovative methods and designs for T1 translation of BSSR.
- To conduct an ongoing evaluation of the success of the skills development course based upon three basic metrics: (a) perceived value; (b) the reach of the course based upon website hits, number of applications, and train the trainer activities; and (c) the impact of the course on the career trajectories of the Fellows (publications, grants).
- To conduct ongoing curriculum development and refinement, as well as dissemination via technology, based upon results of quantitative and qualitative evaluations and new methodologies emerging over the grant period.
Funding Agency: National Cancer Institute, National Institutes of Health (1R25CA244065-01A1)

To read more about this study and the team members involved please visit the CTBS News Article or the ORBIT Institute Website.

Principal Investigator: Zhe He, Ph.D
Specific Aims: Clinical studies are essential in evidence-based medicine. However, participant recruitment has long been a major concern. Although ~60% of new all cancer cases occur among older adults, they comprise merely 25% of participants in cancer clinical studies. Unjustified or overly-restrictive eligibility criteria are the most important modifiable barriers causing low accrual, early termination, and low generalizability.

This in turn can cause the studies to be underpowered and increase the likelihood of adverse drug reactions and toxicity when moved into clinical practice. We are developing data-driven methods and tools to assess the generalizability of clinical studies using the electronic data in clinical trial registries, public patient databases, and clinical data warehouses. This project aims to improve the representation of underserved population subgroups in clinical studies such as older adults with multiple chronic conditions.
Funding Source:

National Institute on Aging (R21 AG061431), Amazon, Eli Lilly and Company, FSU Council on Research and Creativity

Principal Investigator: Zhe He, Ph.D
Specific Aims: Various healthcare information systems such as EHRs have integrated well-curated biomedical controlled vocabularies, e.g., the International Classification of Diseases (ICD) and RxNORM, as their vocabulary foundation. With rich medical concepts linked by hierarchical and associative relationships, these vocabularies and ontologies can also be utilized in health data analytics tasks such as natural language processing, data integration, and decision support. Opportunities exist for leveraging semantic methods to enhance these data science efforts.

Our research and development effectively use biomedical ontologies and/or semantics methods to address important problems in biomedicine and fundamental problems in natural language processing such as word sense disambiguation, relation extraction, and temporal information extraction. In addition, we also seek to build effective machine learning models to predict health outcomes for patients such as mortality and readmission.

Principal Investigator: Zhe He, Ph.D
Specific Aims: The OneFlorida Clinical Data Research Network (CDRN) is a collaborative statewide network that seeks to improve health research capacity and opportunities in the State of Florida through the facilitation of clinical and translation research in communities and health care settings. Its core resource - OneFlorida Data Trust, contains longitudinal and linked patient records of ~15 million Floridians from various sources, including Medicaid/Medicare, cancer registry, vital statistics, and electronic health records (EHR) from its clinical partners.

The Data Trust follows the PCORnet Common Data Model (CDM), and contains detailed patient characteristics and clinical variables, including demographics, encounters, diagnoses, procedures, vitals, medications, and labs. We will help FSU investigators define cohort discovery queries over this rich clinical data warehouse to answer critical research questions

Specific Aims: The National Institutes of Health sponsored a cross-institute, two-day "Workshop on Innovative Study Designs and Methods for Developing, Testing and Implementing Behavioral Interventions to Improve Health" to review, evaluate, and disseminate a selection of innovative designs and analytic strategies for use in behavioral intervention studies. Experts from the behavioral, biostatistical and clinical communities reviewed the utility of new, innovative and potentially more efficient study designs and methods to develop, optimize, test and implement behavioral interventions across the translational targeting multiple behavioral risk factors (e.g., adherence, diet, physical activity, smoking). Presentations and discussions focused mainly on the development and preliminary testing of behavioral interventions on Day 1, with an emphasis on later-stages of development, including testing and implementation of interventions within clinical and community contexts, on Day 2.

Access agenda and resources here.