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.
Bayesian approaches provide a variety of new opportunities for efficient and flexible clinical trials. Here are a few examples of what Bayesian methods enable:
Bayesian model-based approaches
Bayesian model-based approaches provide a formal mathematical method for combining external information with current information at the design stage, during the conduct of the trial, and at the time of analysis. This allows efficient design, lower trial cost, better quantitative decision-making and faster drug development.
Bayesian approaches for fast, flexible learning
The COVID-19 pandemic has elevated the challenge of designing and executing clinical trials within a substantially shortened time frame, and with limited data on the course of a newly emerged disease. The sense of urgency incites clinical researchers to invoke innovative trial design approaches to expedite the identification of efficacious interventions without compromising patient safety and scientific rigor. Bayesian statistical methods are very well suited to address these challenges due to their ability to adapt to knowledge that is gained during a trial.
Bayesian meta-analysis and data pooling
Bayesian methods have become instrumental for updating datasets to suit the needs of new trials. Sponsors are able to optimize on the data they already have through precise analyses of data already collected.
The Virtual Symposium
The Virtual Symposium by Cytel starting on October 8, 2020, will cover a range of topics presented by Bayesian experts in academia, regulatory and industry. The presentations will include:
Adaptive design, platform studies, complex innovative designs using Bayesian modeling
Regulatory acceptability of Bayesian designs
Leveraging external information using Bayesian priors
The Interactive Workshop by Novartis is going to be presented by Simon Wandel and Beat Neuenschwander. It will convey the essentials of Bayesian clinical trials, including fundamentals of Bayesian statistics, derivation of prior distributions, and the planning and evaluation of these trials. Participants will apply what they learn in practical sessions using freely available software (R, JAGS or WinBUGS). You can benefit by learning the concepts of Bayesian statistics and understanding how to leverage historical information using robust priors. You will also be trained on how to plan and analyze Bayesian clinical trials.
Click the button to see the full program and register for the event.