Richard Zink
Vice President of Data Management, Biostatistics, and Statistical Programming at Lexitas Pharma Services

Richard C. Zink is Vice President of Data Management, Biostatistics, and Statistical Programming at Lexitas Pharma Services where he is tasked with developing in-house biostatistics and programming capabilities and leading both biostatistics and data management in the design and analysis of ophthalmology clinical trials on behalf of their clients. Prior to Lexitas, Richard spent 17 years in and around medical product development at a real-world data company where he led data management and statistics in the analysis and reporting of data derived from electronic medical records; a software company where he developed and supported platforms to analyze and visualize safety and data quality outcomes from clinical trials; and pharmaceutical companies where he served as lead statistician for numerous clinical trials. 

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 Methods, and a contributor to six other books on statistical topics in clinical trials and clinical research. He holds a Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill, where he serves as Adjunct Assistant Professor of Biostatistics. Richard was awarded the distinction of Fellow of the American Statistical Association in 2020.

 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®

By Richard C. Zink

This book shows you how to improve efficiency while reducing costs in clinical trials with centralized monitoring techniques using JMP and SAS.

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