Data in Clinical Research

Data in Clinical Research

Data science is good for researchers, good for innovation, and good for public health.

Our Thinking

Clinical research generates tremendous amounts of valuable data. However, for decades, this priceless information has been largely unavailable to the wider community of researchers and its full benefit has gone unrealized. By unlocking these vast storehouses of data, we open a new world of possibilities in which the search for new treatments is accelerated and a better understanding of human health and illness is within reach.

The question at the center of the open-science discussion is not whether data should be shared, but how we can develop responsible methods for doing so. Our collaboration with partners in industry, academia, and other spheres will allow data to be shared in ways that can improve public health worldwide.

Frank Rockhold, PhD
Professor of Biostatistics and Bioinformatics

"We believe the clinical research enterprise needs to come together to build on what exists and create a simpler model for truly open and responsible clinical trial data sharing."

RockholdPortrait

Creating A Common Data Model Across PCORnet

Keith Marsolo, PhD, explains why it is critical to standardize data before using it to answer questions in research, and sheds light on how DCRI is approaching this work via the PCORnet, the National Patient-Centered Clinical Research Network, for which the DCRI is the coordinating center. Learn more about Marsolo's work on the common data model by reading this article.

Highlights

Michael Pencina, PhD

Making Sense of Big Data

Researchers today have access to tremendous amounts of data. But how can they make the best use of this information? Michael Pencina, PhD, writes in STAT about the common mistakes often made when dealing with big data.

Efficiencies for Open Data

Frank Rockhold, PhD, writes in Clinical Trials about why open data is so important to clinical research and outlines suggestions for streamlining data sharing processes.

Eric Peterson, MD, MPH

The Potential of Machine Learning

In a JAMA Viewpoint piece, Eric Peterson, MD, MPH, writes that a focus on adoption of new models is needed in order to fully leverage the potential that machine learning offers.

Biomedical Data Science Across Duke University

Laine Thomas, PhD, a DCRI faculty member, and David Page, PhD, chair of Duke Biostatistics & Bioinformatics discuss the future of biomedical data science at Duke, the role DCRI can play, and how machine learning techniques will be incorporated in this work.

ThomasPage

Data in Clinical Research Thought Leadership

Frank Rockhold, PhD

Professor of Biostatistics and Bioinformatics

Frank Rockhold, PhD, is a professor of biostatistics and bioinformatics at the DCRI. A leading biostatistician in the pharmaceutical industry, Rockholdhas more than 30 years of diverse experience in clinical trial design, data monitoring, decision sciences, statistical research, and epidemiology.

Rockhold was most recently senior vice president of Global Clinical Safety and Pharmacovigilance at GlaxoSmithKline, and prior to that ran Cardiovascular and Metabolic Development and the Biostatistics, Epidemiology and Healthcare Informatics departments. Earlier in his career, he held positions with Merck Research Laboratories and Lilly Research Laboratories.

He served on the board of directors of the Clinical Data Interchange Standards Consortium, most recently as Chairman, is past president of the Society for Clinical Trials, is a fellow of both the American Statistical Association and the Society for Clinical Trials, and is an accredited professional statistician. Rockhold received his PhD in biostatistics from the Medical College of Virginia, his ScM in biostatistics from The Johns Hopkins University, and his BA in statistics from the University of Connecticut.