2016 CCIA Administrative Supplements Projects

Enhancing Network Capacity by Disseminating State-of-the-Art Methods and Tools for the Design and Analysis of Randomized Clinical Trials

Johns Hopkins University

Principal Investigator: Daniel E. Ford, M.D.

Contact: Daniel Scharfstein (dscharf68@gmail.com; dscharf@jhu.edu)

Website: http://www.projectdidact.org/

Representatives of the Johns Hopkins University, University of Michigan, Harvard University and Tufts University CTSA Program hubs will collaborate to disseminate state-of-the-art methods and tools for the design and analysis of randomized clinical trials. Topics being addressed include:

  • Sequential, multiple assignment randomized trial designs;
  • Leveraging baseline covariates to improve the efficiency of randomized trials;
  • Causal analysis of pragmatic trials;
  • Sensitivity analysis for randomized trials with missing outcome data; and
  • Heterogeneity of treatment effects and individualized treatment effects.

The goal is to bring a full menu of critical methods and tools to the clinical and translation research community, and to help foster a common understanding that will increase the likelihood of successful implementation of the methods.

Optimizing Translational Veterinary Trials to Advance Human Outcomes

Ohio State University

Principal Investigator: Rebecca D. Jackson, M.D.

Contact: Cheryl London

Website: https://ctsaonehealthalliance.org/

There is increasing evidence that spontaneous diseases in veterinary patients represent a unique tool to generate critical data regarding the safety and efficacy of novel drugs and devices. Representatives of the Ohio State University, Tufts University, University of Minnesota and University of California-Davis CTSA Program hubs will facilitate the incorporation of large animal models of spontaneous disease into Investigational New Drug studies by creating and implementing standard operating practices and procedures for veterinary clinical trials across the four sites. Specific aims include:

  • Optimizing and distributing a set of standard operating practices for the conduct of veterinary trials;
  • Generating and implementing a veterinary Good Clinical Practice training module;
  • Establishing REDCap for clinical trial management and reporting across CTSA One Health Alliance sites;
  • Forming a data safety management board to oversee trials and facilitate institutional approval; and
  • Developing a cohesive outreach effort to ensure consortium-wide enrollment.

Successful completion of this project will enable seamless initiation of veterinary clinical trials over multiple sites, thereby establishing a well-organized, proficient network that can rapidly provide critical information to accurately inform subsequent human translational efforts.

Real-Time Genomic Analysis Using iobio

University of Utah*

Principal Investigator(s): Carrie L. Byington, M.D., Willard H. Dere, M.D., F.A.C.P.

Contact: Willard Dere

Website: http://iobio.io/

Genomic analyses promise to revolutionize the diagnosis and treatment of inherited disease and cancers. However, many current genomic analysis tools are not accessible to clinicians and medical researchers because they require extensive bioinformatics training, hours or days of analysis time and produce static output files requiring expert processing and interpretation. Researchers at the University of Utah CTSA Program hub aim to improve diagnostic rates and maximize returns from DNA sequence data by incorporating a web-based analysis system, iobio, to empower all biological researchers to analyze — easily, interactively and in a visually driven manner — large biomedical data sets into clinical practice at two Utah CTSA undiagnosed disease clinics. Specifically, this supplement will:

  • Close the gap between computational and clinical genomics by training physicians to interact directly with genomic data and results; and
  • Increase the diagnostic rate in complex clinical cohorts of families.

With the help of visually-guided tools in iobio, physicians will be able to examine patient data directly, assess its quality, and identify potentially missed disease-causing variants, thereby leading to increased diagnostic rates, better health outcomes and reduced health care costs.

*This award reflects co-funding support from the NIH Big Data to Knowledge Initiative (NIH Common Fund, through the Office of Strategic Coordination/Office of the NIH Director).