Richard Zink
Director of Statistics at TARGET PharmaSolutions

Richard C. Zink is Director of Statistics at TARGET PharmaSolutions, an innovative company that organizes a community of stakeholders to generate real-world insights for several diseases, primarily in hepatology and gastroenterology. Richard joined TARGET PharmaSolutions in 2018 after seven years in the JMP Life Sciences division at SAS Institute, where he developed software to assess safety and data integrity in clinical trials utilizing CDISC standards. Prior to SAS, he spent eight years in the pharmaceutical industry. Richard is the 2018 Chair-Elect of the Biopharmaceutical Section of the American Statistical Association, host of the Biopharmaceutical Section Statistics Podcast, and Associate Editor for the DIA journal, Therapeutic Innovation & Regulatory Science. His research interests include data visualization, the analysis of pre- and post-market adverse events, subgroup identification for patients with enhanced treatment response, and the assessment of data integrity in clinical trials, and he participates in scientific working groups for safety and real-world evidence. Richard is the author of Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS, the co-editor of Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Method s, and a contributor to six other books on statistical topics in clinical trials and clinical research. He holds a PhD in Biostatistics from the University of North Carolina at Chapel Hill, where he serves as Adjunct Assistant Professor of Biostatistics.

Books By This Author

Modern Approaches to Clinical Trials Using SAS®: Classical, Adaptive, and Bayesian Methods

Edited by Sandeep Menon and Richard C. Zink

This book thoroughly covers several domains of modern clinical trial design: classical, group sequential, adaptive, and Bayesian methods that are applicable to and widely used in various phases of pharmaceutical development.

Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP® and SAS®

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