Cytel Blog

Measuring treatment effect during a clinical trial is often the source of much debate, particularly during rare disease trials that must stimulate investigations using small samples. Unlike statistically significant results, for which there are many tests, meaningful measures of treatment effect are still under development (Kieser 2012). Cytel statistician Ursula Garczarek wonders whether this holds true in the realm of small samples and small target populations. After all, does the summary statistic in such a small trial rely on many assumptions that might not correlate with reality?

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’s New Horizons Webinar Series introduces you to the latest innovations in statistical trial design. This webinar from the series is presented by Dr. Yuan Ji, a consultant for Cytel. Yuan is the founder of Laiya Consulting and currently is the Professor of Biostatistics at The University of Chicago. In his presentation, Professor Ji introduces the U-Design version 1.4, which mainly consists of a new module of dose-finding trial designs with joint efficacy and toxicity outcomes.

Click the button to register for the next webinar in this series, presented by Cytel's Ursula Garczarek. Ursula will be presenting a case study on the value of detailed clinical trial simulations for rare diseases.

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.

COVID-19 has created extreme uncertainties -- a dearth of historical information combined with the need for safety, statistical rigor, and speed has prompted the rapid surge in the generation of clinical data. However, this information is scattered across multiple platforms, making it challenging to measure comparative treatment effects across trials. Consequently, we are seeing a high frequency of failures, that diminishes the public’s confidence in research and slows the path from scientific results to action.

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.

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.

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.