The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
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 Introduces Advanced Design Framework: Part 1 - Methods for Thorough Exploration of Design Space
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.
Even before the era of COVID-19, significant attention was channeled to the overwhelming potential of adaptive MAMS designs. Short for multi-arm multi-stage designs, these trials enable numerous therapies to be tested on a single platform with a single comparator arm. When patients are too few or there are several therapies in competition with each other to enroll, adaptive MAMS designs expedite the discovery of new drugs.
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.
Innovation in trial designs are offering new routes forward for organizations of any size. They are now also aligned with the overarching goals of improved clinical development, better pre-planning, greater patient safety, less medical waste, and/or increased knowledge.
Cytel is hosting "New Horizons Webinar Series" that will introduce biostatisticians to the latest innovations in statistical trial design. The first webinar in the series on Adaptive Multi-arm Multi-stage Clinical Trials is going to be presented by Cytel's President & Co-Founder, Cyrus Mehta. Click on the button to register.
Cytel’s co-founder, Nitin Patel, conducted a webinar on designing clinical trials from a program-level perspective. His presentation helps us understand the value of designing clinical trials considering downstream consequences. Watch the on demand webinar to get insights on the role of simulation in optimizing clinical trials' performance from a program perspective and understanding the hybrid Bayesian-frequentist approach to clinical trial design.
We also had the opportunity to interview Nitin about his journey since he co-founded Cytel and got his views on implementing a program-wide strategy for pharma and biotech companies. Read the blog here.
Continue reading this blog for key highlights from the webinar.