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Parameter Optimization of Multi-Arm Multi-Stage Designs

Innovations in the process of designing adaptive clinical trials have unlocked new possibilities for designing and executing more efficient, cost-effective, and ethical clinical trials. As more adaptive methods are introduced, biostatisticians struggle to optimize the growing number of interdependent trial parameters and ensure control over various execution assumptions. These trends are especially true for multi-arm multi-stage trial designs, which are typically conducted earlier in the drug development cycle and are therefore gaining prominence as decision points in the product’s evidence-generation roadmap.

Many design and optimization efforts are conducted using custom code, which allows for the flexible processing of increasingly complex tasks and design characteristics. But using languages such as R requires specialized training and expertise. Coding work, contingent on the statisticians’ abilities and experience, can be error-prone and time-consuming.

To learn more about how Solara® enables trial optimization and parameter selection in a systematic and holistic way using a combination of cloud computing and design-selection algorithms, download our new complimentary white paper, “Leveraging Cloud Computing and Advanced Software for Parameter Optimization of Multi-Arm Multi-Stage Designs”:


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Our new white paper is featured as part of our winter Weekend Reads series, which includes complimentary publications on a variety of topics on clinical trial design and data science. Subscribe to our weekly newsletter below and never miss a post!

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