OPTIMIZE CLINICAL STUDY DESIGN WITH RANDOMIZATION SIMULATION
Sponsored by: Anju Software
- Clinical Study Design
Date: 18 November
Time: 3PM London/10AM New York
The power of simulations for initial design and adaptive trial changes
Clinical Trial designs are becoming more complex and the pressure to streamline and accelerate clinical trials continues to increase. Nonetheless, it remains critical to carefully design randomization methods that will result in a final patient distribution that is optimized for statistical analyses. Delays in designing randomization schemes and subsequent validation of methods can negatively impact trial start-up timelines. In adaptive trials, the impact of this delay is multiplied every time the design of the trial is adjusted. To maintain speed and the appropriate rigor in randomization design it is important to be able to quickly and reliably assess multiple designs and rapidly simulate results, even when using dynamic randomization techniques.
Dynamic randomization algorithms can offer several advantages when used in a clinical trial over the more commonly used permuted block method. However, implementing a minimization algorithm, such as Zelen’s method or Pocock & Simon, involves more difficult computational work and requires specifying multiple parameters that affect the outcome of the randomization. It can be a significant challenge to determine the optimum parameters for a particular study design and has the potential to introduce delays in study startup timelines.
A Randomization Simulator permits a statistician to create a simulation of a trial and obtain results from a specified subject listing. Multiple simulations can easily be created adjusting the randomization parameters, or with different random number seeds to assess the impact of adjusting the values and estimating the range of random variation in the results. The results of this analysis can be used to determine the optimum settings (including the weighting of treatment groups and stratification factors). Once the final settings are decided, this type of simulation can also be used to validate the randomization configuration in your RTSM/IRT.
Join us to learn from industry experts to learn more about how automated randomization simulations can support the use of dynamic randomization techniques without negatively impacting your study timelines.
Tony Warren is a Product Manager at Anju Software. Tony holds a B.S. from Purdue University and an M.S. from the University of Arizona. After serving in the US Air Force as a scientific programmer he spent time developing software solutions in the financial and manufacturing industries. His experience in the Life Sciences industry began when he joined an emerging CRO, Kendle International, as a developer for a new phone-based (IVRS) randomization system – TriaLine. Tony spent 13 years with Kendle expanding the use of TriaLine and leading the transition from a phone-based to a web-based solution. In 2018, he joined OmniComm (now part of Anju Software) as Product Manager where he is responsible for the direction and growth of Anju’s RTSM solution, IRTMaster.
VP Product Management
Wendy lives at the intersection of life sciences and technology, dedicating her 25+ year career to both. Passionate about improving patient outcomes by focusing on technology solutions that help Academic Institutions, Biotech, Pharmaceutical, and Medical Device companies discover and deliver new therapies to the market faster and safer. She is most inspired by challenges that bring amazing people with relentless “get it done” attitudes together to solve complex problems.
President, The Swearingen Group LLC
Dr. Swearingen is a consulting statistician more than two decades of experience in leading the design, development, and statistical analysis of clinical outcome measures and their implementation in clinical trials. Dr. Swearingen also is the Vice-President of Clinical Outcomes and Analytics for Samumed LLC in San Diego. Prior to Samumed, he was an Associate Professor with tenure in the Department of Pediatrics at the University of Arkansas for Medical Sciences and served as Associate Director of the Pediatric Biostatistics Program at Arkansas Children's Hospital Research Institute, where he led informatics and analytic teams in the design and conduct of community-based clinical trials. Dr. Swearingen has over 65 peer-reviewed publications and served on several NIH grant review panels. Dr. Swearingen received his bachelor’s degree in Psychology from Belmont University, and his master’s and doctoral degree in Biostatistics from the Medical University of South Carolina.
Key Learning Objectives
- Components of a system for randomization and clinical trial supplies management
- Introduction to dynamic minimization based on Zelen and Pocock & Simon algorithms
- Building a simulation and archiving results
- Creating variants of existing simulations in a batch mode to enable a large suite of results to be created for detailed analysis.
- Statistical Programming
- Clinical Data Management
- Clinical Operations
- Clinical Trial Supply Management