Michael J Pencina
Professor of Biostatistics & Bioinformatics

As Vice Dean for Data Science, Dr. Pencina is responsible for developing and implementing quantitative science strategies as they pertain to the education and training, and laboratory, clinical science, and data science missions of the School of Medicine. Dr. Pencina is a Professor of Biostatistics and Bioinformatics at Duke University and Director of Duke AI Health. Previously, he served as Director of Biostatistics at the Duke Clinical Research Institute.

Dr. Pencina is an internationally recognized authority in risk prediction model development and evaluation. Expert panels and guideline groups frequently recommend methods proposed in his research and have adopted them as the most promising new statistical tools in assessing and quantifying model performance.

Dr. Pencina is actively involved in the design, conduct and analysis of clinical studies with particular focus on novel and efficient designs and applications of machine learning for medical decision support. He interacts regularly with investigators from academic and industry institutions as well as the Food and Drug Administration. 

Thomson Reuters/Clarivate Analytics recognized Dr. Pencina as a Highly Cited Researcher in two fields, social sciences and clinical medicine, for the years 2014 – 2020. He is co-author of more than 370 manuscripts published in peer-reviewed journals and has been cited over 90,000 times in professional publications. He serves as Deputy Editor for Statistics at JAMA-Cardiology and Associate Editor for Statistics in Medicine.

In 2003, Dr. Pencina received his PhD in Mathematics and Statistics from Boston University. He holds master’s degrees from the University of Warsaw in actuarial mathematics and business culture. He joined the Duke University faculty in 2013. Dr. Pencina served as an Associate Professor in the Department of Biostatistics at Boston University and the Framingham Heart Study and as Director of Statistical Consulting at the Harvard Clinical Research Institute.

strategies as they pertain to the education and training, and laboratory, clinical science, and data science missions of the School of Medicine. Dr. Pencina is a Professor of Biostatistics and Bioinformatics at Duke University and Director of Duke AI Health. Previously, he served as Director of Biostatistics at the Duke Clinical Research Institute.

Dr. Pencina is an internationally recognized authority in risk prediction model development and evaluation. Expert panels and guideline groups frequently recommend methods proposed in his research and have adopted them as the most promising new statistical tools in assessing and quantifying model performance.

Dr. Pencina is actively involved in the design, conduct and analysis of clinical studies with particular focus on novel and efficient designs and applications of machine learning for medical decision support. He interacts regularly with investigators from academic and industry institutions as well as the Food and Drug Administration.

Thomson Reuters/Clarivate Analytics recognized Dr. Pencina as a Highly Cited Researcher in two fields, social sciences and clinical medicine, for the years 2014 – 2020. He is co-author of more than 370 manuscripts published in peer-reviewed journals and has been cited over 83,000 times in professional publications. He serves as Deputy Editor for Statistics at JAMA-Cardiology and Associate Editor for Statistics in Medicine.

In 2003, Dr. Pencina received his PhD in Mathematics and Statistics from Boston University. He holds master’s degrees from the University of Warsaw in actuarial mathematics and business culture. He joined the Duke University faculty in 2013. Dr. Pencina served as an Associate Professor in the Department of Biostatistics at Boston University and the Framingham Heart Study and as Director of Statistical Consulting at the Harvard Clinical Research Institute.

Current Research Interests

  • Development and assessment of performance of risk prediction models
  • Use of electronic health records (EHR) for predictive medicine
  • Assessment of the usefulness of new biomarkers
  • Optimal treatment guidelines and resource utilization
  • Risk communication strategies:  novel approaches
  • Design of clinical trials with composite outcomes
  • Data monitoring committees (DMC):  role, mandate and operation

Current Appointments & Affiliations

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