Cytel Blog

The current state of the clinical trials industry faces a challenge that was only hypothetical three or four years ago. Thanks to the advent of cloud-computing and advances in simulation technology, sponsors can now design hundreds of thousands of clinical trials in less than an hour. Yet how do we choose amongst all of these myriad options in a way that optimizes commercial prospects? Cytel’s Chief Scientific Officer sits down with us to discuss the Re-imagined Clinical Trial.

As a part of Cytel’s "New Horizons Webinar Series", Alind Gupta, Senior Data Scientist, presents case studies from his research on applying machine learning for predictive analysis and evidence generation.

The biopharmaceutical and healthcare industries now collect more data than ever before due to advances in the variety of information sources combined with the ability to store vast quantities of diverse data. Sophisticated machine learning (ML) and AI techniques allow us to access and analyze any combination of a multitude of data sources. The way that traditional controlled sources are viewed is being adapted in light of new evidence that emerges from real-world data. In his presentation, Alind introduces us to the concept of ML and Causal Inference and discusses case studies from randomized clinical trials and real-world data.

Click the button to register for the on demand webinar.

MUCE is a Bayesian solution for cohort expansion trials where multiple dose(s) and multiple indication(s) are tested in parallel. Such methods are particularly important for areas like oncology where several doses and several indications must be tested for successful completion of early phase trials, and optimal choice of dose and population to move on from early phase to a reasonable dosage for Phase 3.

Note that for these situations the number of comparator arms for a trial can increase rather rapidly. Testing three doses with three indications essentially requires 9 different trials. An efficient way to test a higher number of trials is therefore necessary for accelerated clinical development.

The widespread use of cloud-computing has altered the clinical trial design process. Whereas three or four years ago, it would take a statistician perhaps two or three days to design five clinical trial designs, a well-resourced statistician can now simulate and model well over 100,000 designs in less than 30 minutes. How does this affect the process of designing clinical trials

According to Yannis Jemiai, Chief Scientific Officer at Cytel, a combination of technology and process changes can establish the foundation for significant increases in productivity. Yannis argues that uncertainty should not be viewed as a challenge but an opportunity. Using statisticians strategically as well as tactically throughout the design process can help R&D teams drive commercial value for greater speed, savings and success.

Platform trials are a new type of clinical trials where multiple interventions can be evaluated simultaneously against a common control group within a single master protocol. Platform trial designs are an extension of adaptive trial designs that are sometimes referred to as a multi-arm, multi-stage (MAMS) design, as multiple interventions (‘‘multi-arm’’) undergoing multiple interim evaluations (‘‘multi-stage’’) are part of the design features.

This autumn Cytel has been holding a number of webinars on Platform Trials, ranging from a discussion with Cyrus Mehta on statistical innovations to incentivize more sponsors to consider platform trials, to next week's event with Jason Connor (Confluence) on the use of Bayesian methods for these innovative trial designs. 

In a recent webinar Jay Park, Director of Trials Research for Cytel in Canada, presented a webinar to review the concept of platform trials and discuss important design considerations for platform trials. Jay is the author of several leading papers on Platform Trials, including one in CA: A Cancer Journal for Clinicians, the journal with the world's highest impact factor. He has also produced a complimentary primer on the subject which you can download here.

Continue reading this blog to get a summary of his talk. Click the button to access the on demand webinar.

Cytel has recently revealed its Advanced Design Framework, a method developed by Cytel’s thought leaders that draws on decades of experience increasing clinical development productivity. The Framework illustrates how advances in design processes and technology can help development teams deliver greater business results, unifying statistics and strategy in the era of cloud computing and making strategic use of well-resourced statisticians.

Cytel has recently revealed its Advanced Design Framework, a method developed by Cytel’s thought leaders that draws on decades of experience increasing clinical development productivity. The Framework illustrates how advances in design processes and technology can help development teams deliver greater business results, unifying statistics and strategy in the era of cloud computing, and making strategic use of well-resourced statisticians.

The framework consists of three parts: Thoroughly Explore, Decide Together, and Communicate Trade-Offs. This week we take a deeper look into the second part of this Framework, revealing how to effectively incorporate varied perspectives to efficiently design innovative clinical trials. Opportunities for quantitative evaluation criteria and design without bias help R&D teams sift through the thousands of trial designs options to optimize for speed, success, and savings.

Cytel has recently revealed its Advanced Design Framework, a method developed by Cytel’s thought-leaders after a decade of fine-tuning clinical development processes. The framework consists of three parts: Thoroughly Explore, Decide Together, and Communicate Trade-Offs.

The Framework demonstrates how to unify statistics and strategy in the era of cloud-computing, by making strategic use of well-resourced statisticians. This week, we take a deeper look into the first part of this Framework, revealing how to explore hundreds of thousands of designs available to sponsors, rapidly and in real-time, to improve the chances of identifying the design that optimizes for speed, success, and savings.

For over a decade, advanced trial design techniques have promised efficient trials with accelerated timelines, reflecting the ability to quantify uncertainty and de-risk trials using adaptive tools. Despite the emergence of these complex innovative designs, the success of Phase 3 trials has continued to hover at 33% while the average time to market remains about 6 years.