The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
As Chief Scientific Officer, Dr. Yannis Jemiai plays a pivotal role in maintaining Cytel’s well-established reputation for statistical excellence and our track-record of bringing innovative analytic approaches to the development of medicines for human health. In this blog, we ask Yannis for his favorite Cytel events from 2020.
As we prepare to close the door on 2020, we asked Pantelis Vlachos, Principal/Strategic Consultant for Cytel, to share his favorite Cytel events of this year. Continue reading this blog for a summary of Cytel’s 2020 contributions around adaptive designs and Bayesian methods.
When designing clinical trials, biostatisticians and clinical development teams are often faced with a conundrum. Given the parameters of their clinical study, they usually begin with five or six possible design options and begin to explore the most promising ones. The likelihood is that none of these trials will be optimal designs. Rather, they meet certain criteria that are “good enough” at which point, clinical development teams might begin to lead one way or another.
As a part of Cytel’s Advanced Design Framework, a new Framework for the statistical design of clinical trials, Cytel discovered that a specific combination of process changes and technological advances has the potential to increase clinical development productivity by 10-20%. The Framework summarizes these as Thoroughly Explore, Decide Together and Communicate Tradeoffs. Here are 7 key features of this improved strategic framework. Alternatively, watch the webinar of our Chief Scientific Officer Yannis Jemiai discussing this Advanced Design Framework.
Increasing Clinical Development Productivity Using Statistics and Cloud-Computing
The need for Re-imagining Clinical Trials: A recent survey conducted by Cytel found that only 42% of respondents reported using any complex or innovative clinical trial designs beyond the familiar group sequential approach. Although regulators respond quite favorably to such designs, sponsors have remained hesitant to use them.
A combination of technological and process advances are necessary to overcome mechanisms that contribute to stagnating statistical innovation in clinical development. Cytel responded by creating this new whitepaper that provides a new strategic framework that can help Clinical Development teams leverage cloud-computing and begin to initiate process changes, necessary to increase development productivity by 10-20%.
Significant advances have been made to enhance the efficiency of clinical trial designs. However, the traditional methods deployed by many pharmaceutical companies are fraught with challenges. Much less consideration is given to the value of decisions in the context of development programs or portfolios.
Cytel recently launched the “C-Suite Webinar Series”, an online initiative to help pharmaceutical executives drive commercial success with strategic insight from statistics. As a part of this series, Zoran Antonijevic, Head of Biometrics at MedSource, conducted a webinar where he describes methods for maximizing the value of programs and portfolios. This event was attended by numerous biopharma leaders.
Continue reading this blog to understand the concepts of program and portfolio optimization and learn about the benefits and opportunities presented by them.
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.