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.
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.
Cytel recently conducted a webinar on Bayesian Dose-finding Designs for Modern Drug Development, presented by Dr. Yuan Ji.
Dr. Ji is a Professor of Biostatistics at The University of Chicago and a well-known name in the industry. In his presentation, he introduces representative Bayesian designs for dose-finding trials. The webinar offers insights on topics including classical DLT-based dose-finding designs, designs with delayed toxicity using time-to-event endpoints, and designs for combination dose-finding trial. Watch the on demand webinar to see the illustration of Bayesian modeling and inference for dose-finding designs that utilize the concept of probability intervals and related methods for clinical development and decision making.
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.
In this two-part blog series, we interview Bart Heeg, Vice President HEOR and Founder at Ingress Health (A Cytel company). Bart provides us insights on the trends in HEOR and explains why Bayesian methods are also important for Health Economics. Read Part 1 here.
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. The introductory webinar “Bayesian Statistics and FDA Regulatory Acceptability” is presented by Greg Campbell, PhD, Former Director of Biostatistics, U.S. Food and Drug Administration.
In the United States Bayesian statistics has been used in regulatory submissions to the Food and Drug Administration (FDA) for confirmatory clinical trials medical devices for more than fifteen years. In this webinar, Dr. Campbell reviews the Bayesian history and accomplishments for medical devices. He talks about the status and opportunities of Bayesian statistics for pharmaceutical drugs and biologicals. We also learn about the challenges and the future of Bayesian statistics in the regulatory environment. You can access the on demand webinar and register for the rest of the series by clicking the button.
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.