Cytel Blog

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.

Staying abreast of the rapid pace of clinical development means adopting innovative or computationally intensive designs like Bayesian methods. These methods allow for the incorporation of prior knowledge, in terms of either expert opinion from clinicians or historical data, in statistical inference. Thus, they have the additional advantage of being able to work with real-world data (generally, real-world data has a lot of missing data) without the need to impute missing values. These kinds of models are also flexible enough to work with temporal data. This helps ease the reliance on large sample approximations that are often required for frequentist methods and generally results in greater efficiency in study design.

In this edition of The Informative Bayesian by Pantelis Vlachos, we learn about information borrowing to form a prior distribution. In a Bayesian framework, borrowing from historical data is equivalent to considering informative priors. These priors can be derived as meta-analytic predictive (MAP) priors or using patient-level data.

Methods involving Group Sequential Designs is one of the earliest deviations from a traditional two-arm clinical trial with no interim looks at the data. They add incredible value to trials through their abilities to safeguard patients, reach positive conclusions early and keep trial designs simple and streamlined.

Sequential trials also help reduce costs and the number of patients involved, but finding a positive conclusion earlier is quite important too. In the drug development process, where patent lifetime is limited, reaching a decision six months or a year earlier is a big advantage. Sample Size Re-estimation is another key tool in the modern trial designer’s toolkit that proves to be useful. Continue reading this blog to learn how to use these methods and understand how they can improve trial design.

Today, there is a need for advanced quantitative techniques to combine all available information for better decision making in health care. Bayesian statistics allow us to make probabilistic inferences on the parameter of interest, which is missing in a traditional frequentist approach. Apart from the philosophical issues, Bayesian analysis provides a practical and intuitive tool for interpretation of study results and risk evaluation of clinical hypotheses.

Cytel and Novartis are together hosting a complimentary Bayesian Virtual Symposium and an Interactive 7-part workshop. This series will expose you to cutting edge topics from industry renowned leaders in Bayesian statistics. Click the button to learn more and register.