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Tyson Rogers

Tyson Rogers, MS

Product Development Strategist, Biostatistics

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Tyson serves as a Principal Product Development Strategist with NAMSA. He received his Master’s Degree in Biostatistics from University of California, Los Angeles (UCLA) and has been working on clinical trials for the past 18 years. He assists companies, both large and small, with the development and execution of clinical evidence plans. His statistical interests and expertise focus on trial design, strategy for interim analyses, adaptive sample size re-estimation and application of Bayesian statistics to clinical trials.


  • Advising on strategy for statistical design and analysis of clinical research studies
  • Serving as a statistical member on Data Safety and Monitoring Boards (DSMBs) for a variety of medical devices
  • Advising on and developing statistical analysis plans and statistical aspects of clinical study protocols
  • Calculating sample sizes and creating randomization schedules
  • Analyzing and reporting data from large, multi-center pivotal clinical trials, as well as small feasibility and pilot studies
  • Working with DSMBs to establish charters and prepare monitoring and interim analysis reports
  • Creating tables and graphs and providing statistical expertise for scientific conference presentations and journal publications, including co-authorship in the New England Journal of Medicine
  • Negotiating with the U.S. Food and Drug Administration (FDA) and other regulatory agencies regarding study design and statistical analyses, including those for the Breakthrough Devices Program
  • Preparing written responses to the FDA and Japan’s Pharmaceuticals and Medical Devices Agency (PMDA) regarding statistical design and analyses


  • Compared study designs using adaptive sample size re-estimation versus fixed sample size group sequential methods to help a Client understand key differences and pros/cons of how each approach uses information available at an interim analysis
  • Created a single-arm non-randomized study design synopsis for an FDA Q-submission (pre-submission), including a well-justified performance goal using synthesis of available publications
  • Developed a Bayesian predictive probability analysis to provide regulatory reviewers with an assessment, based on the available data at the time of submission, of the likelihood that long term outcomes will be favorable at the conclusion of the study