Matthew A. Psioda, PhD
Matthew A. Psioda, PhD
Matthew Psioda is an assistant professor in the Department of Biostatistics within the Gillings School of Global Public Health at the University of North Carolina at Chapel Hill. He has more than 12 years of experience as a practicing biostatistician in the pharmaceutical development industry. He has worked on a wide variety of clinical trials, including phase II dose-finding trials, confirmatory phase III trials, and integrated summaries of efficacy and safety (ISS/ISE). His methods research includes the development of statistical methods for Bayesian clinical trial design with a focus on methods for incorporating prior information (e.g., historical data) as well as Bayesian adaptive designs that seek to optimize the efficiency of early phase trials (e.g., oncology basket trials). He is an expert statistical advisor for the Office of Biostatistics in FDA/CDER, currently working on the development and application of Bayesian approaches for complex innovative trial designs.
Dr. Psioda has a great appreciation of his role as an educator and mentor to students on the appropriate use of statistical methods. He actively teaches in the department and advises graduate students.
Honors and AwardsBarry H. Margolin Dissertation Award
2016FDA ORISE Fellow
2015-2017Genomics and Cancer Training Grant recipient
Introduction to Statistical Computing and Data Management (BIOS 511), Fall 2016
- Bayesian Clinical Trial Design
- Bayesian Computation
- Complex Innovative Trial Designs
- Pragmatic Clinical Trials
- Type II Diabetes
- Cardiovascular Disease
- Biostatistics Computing Committee, Chair, 2016-Current
- Gillings One MPH Steering Committee, 2018-2019
- Public Health Data Science Committee, 2019- Current
- NIH Back Pain Consortium, Executive Committee, Chair, 2019-Current
A Practical Bayesian Adaptive Design Incorporating Data from Historical Controls. M. A. Psioda, M. Soukup, and J. G. Ibrahim (2018). Statistics in Medicine, 37(27), 4054-4070.
Bayesian Clinical Trial Design Using Historical Data That Inform the Treatment Effect. M. A. Psioda and J. G. Ibrahim (2019). Biostatistics, 30(3), 400-415.
Bayesian Adaptive Basket Trial Design Using Model Averaging. M. A. Psioda, J. Xu, Q. Jiang, C. Ke, Z. Yang, and J. G. Ibrahim (in press). Biostatistics.
Bayesian Design of Biosimilars Clinical Programs Involving Multiple Therapeutic Indications. M. A. Psioda, K. Hu, Y. Zhang, J. Pan, and J. G. Ibrahim Biometrics.
Bayesian Design of a Survival Trial with a Cured Fraction Using Historical Data. M. A. Psioda and J. G. Ibrahim (2018). Statistics in Medicine, 37(26), 3814-3831.
PhD, Biostatistics, University of North Carolina at Chapel Hill, 2016
MS, Mathematics, University of North Carolina at Wilmington, 2006
BS, Mathematics, University of North Carolina at Wilmington, 2000