Translating Data Into Action
The DCRI pushes beyond what’s most apparent. By tapping the experience of Duke faculty statisticians and the real-world insights of practicing physicians, we find the question that most effectively probes the challenge in your research and the fullest potential in data it generates. The result: meaningful decisions reached more quickly and efficiently, evidence that has more immediate impact on patient care and datasets with value beyond the immediate analyses.
View Insights from the Team
Where Do We Go From Here?
Utilizing rigorous, best-in-class tools and mathematical algorithms, our renowned biostatisticians and data scientists can tailor methods to the question. We find patterns within datasets that lead to stronger and more actionable insights. This approach of framing the right question to provide context and focus and applying optimal methods marks our work in:
- Clinical trial design and risk-based monitoring
- Independent data monitoring support
- Clinical trial statistics
- O2E: Program for Outcomes, Endpoints, and Estimands
- Comparative Effectiveness Research
- Center for Predictive Medicine
- Data sharing
Dam Data: Health Systems, Machines, And Learning
Machine learning allows practitioners to reach beyond their own experiences and access the sum of experiences of all patients in the health system. It mimics how physicians think about treatment options, but at a scale that can only be achieved with computing.
But machine learning is not sufficient on its own. Clinical expertise, study design, and deep understanding of the data to be analyzed are as critical as the machine learning methods to be applied.
Can Randomized Pragmatic Trials Fulfill Their Promise?
Michael Pencina, PhD and Eric Peterson, MD, and co-authors from McKinsey & Company, describe the steps that researchers, the private sector and regulators can take to design and implement clinical trials that are better, faster, and cheaper using real world evidence.
Illustrating Informed Presence Bias in EHRs
EHR data have a number of appealing characteristics, but they also pose a number of analytic challenges. How a patient interacts with a health system can influence which data are recorded in the EHR. Benjamin Goldstein, PhD, and his colleagues explore the implications.
Three Risks to Avoid in the Rush for Health Care Big Data
How can we ensure that big data in health care will be the rich resource we imagine? In this commentary for STAT, Michael Pencina, PhD, likens the clamor to collect more and larger datasets to the 19th century Gold Rush and describes three risks that data scientists and data consumers must avoid.
Our Partners
Duke Margolis Center
Duke-Margolis brings together capabilities that generate and analyze evidence across the spectrum of policy to practice, supporting the triple aim of health care – improving the experience of care, the health of populations and reducing the per capita cost.
Duke Department of Population Health Sciences
The Duke Department of Population Health Sciences engages faculty members from a variety of disciplines — including epidemiology, health services research and policy, health economics, health measurement and behavior, and implementation science — who work to answer complex questions about the drivers of health in large populations.
Duke AI Health
Duke AI Health is Duke University’s center for artificial intelligence health research. Based at the Duke University School of Medicine, the center performs cutting-edge quantitative research, including foundational research, application development, technology infrastructure, and model assessment methodology.