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.

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.

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.

Pharmaceutical and biotech companies are under pressure to deliver more and deliver faster with fewer resources. The cost of drug development, failure rate and human cost associated with prolonged participation in a trial turn out to be steep in case of an ineffective trial. As the industry seeks new levels of clinical trial efficiency and probability of success, more companies are looking to use advanced, innovative and computationally intensive designs like Bayesian methods.

Bayesian methods are of growing interest to the drug development industry, as they allow clinical investigators to leverage historical trial data as well as learnings from new data as it accrues throughout a trial. The result is better-informed decision making, greater program flexibility, and the ability to run smaller, more resource-efficient trials.

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.

The combination of greater access to electronic health records, bigger electronic claims datasets, and the need for more clinical insight in ensuring patient safety, has made observational studies an important new tool in trial design. Observational studies typically take non-randomized data from outside of a trial and use quantitative and modeling techniques to draw conclusions from big datasets. While typically used for HEOR and market access, augmenting regulatory submissions with observational studies is gaining prominence. As with all data analyses, there is an implicit rule of ‘garbage in-garbage out,’ where data that is not up to the standard required for the formation of sound scientific judgment, should not be used. Sponsors should rely on the most sophisticated tools and advanced analytics to make the most rigorous use of available data.

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.