Innovations In Bayesian Trials

Virtual Symposium and Interactive Workshop
Brought to you by Cytel and Novartis

Cytel and Novartis are excited to invite you to our complimentary Bayesian Virtual Symposium and Interactive Workshop. Join us for this series, which will expose you to cutting edge topics from industry renowned leaders in Bayesian statistics. 

Virtual Symposium

This dynamic program 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

Interactive Workshop

This 7-part workshop, brought to you by Simon Wandel and Beat Neuenschwander, Novartis Pharma AG, Basel conveys 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). At the end of the workshop, participants will be able to:

  • Understand the concepts of Bayesian statistics
  • Leverage historical information through the use of robust priors
  • Plan and analyze Bayesian clinical trials
  • Communicate the value of using historical information in clinical studies

Virtual Symposium

Brought to you by Cytel

 

Thursday, October 8, 10AM EDT
Bayesian Statistics and FDA Regulatory Acceptability
Greg Campbell, PhD, President, GCStat Consulting (1hr)

Thursday, October 22, 11AM EDT
Bayesian Dose-finding Designs for Modern Drug Development
Yuan Ji, Professor of Biostatistics, Department of Public Health Sciences, The University of Chicago (1hr)

Date TBC 
Bayesian Dynamic Borrowing for Partial Extrapolation and Bridging Studies: Methods and Case Studies
Nicky Best, Head, Advanced Biostatistics and Data Analytics Centre of Excellence, GSK (1hr)

Thursday, November 19, 10AM EDT
Statistical Design and Conduct of Platform Trials
Jason Connor, President & Lead Statistical Scientist, ConfluenceStat (1hr)

Thursday, December 3, 10AM EDT
Bayesian Models for Precision Oncology Clinical Trials
Peter Mueller, Professor, Department of Mathematics and the
Department of Statistics & Data Science, The University of Texas at Austin (1hr)

Thursday, December 10, 11AM EDT
Recent Development on Bayesian Clinical Trial Designs Using Historical Data
Ming-Hui Chen, Professor and Head of the Department of Statistics, The University of Connecticut (1hr)

Presentation Details Coming Soon! Ram Tiwari, Director, Division of Biostatistics, FDA 

Interactive Workshop

Brought to you by Novartis

 

Thursday, October 15, 9AM EDT
Interactive Workshop 1: Essentials of Bayesian Statistics (2 hrs)

Thursday, October 29, 9AM EDT
Interactive Workshop 2: Bayesian Meta-analysis and Hierarchical Models (2 hrs)

Thursday, November 12, 9AM EDT
Interactive Workshop 3: Prior distributions (Part I) (2 hrs)

Thursday, January 21, 9AM EDT
Interactive Workshop 4: Prior distributions (Part II) (2 hrs)

Thursday, February 4, 9AM EDT
Interactive Workshop 5: Survival analysis (Part I) (2 hrs)

Thursday, February 18, 9AM EDT
Interactive Workshop 6: Survival analysis (Part II) (2 hrs)

Thursday, March 4, 9AM EDT
Interactive Workshop 7: Other topics & Wrap-up
(2 hrs)

Register

Check off your choice of sessions below:

 

Abstracts

 

Bayesian Statistics and FDA Regulatory Acceptability, 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. The Bayesian history and accomplishments for medical devices will be reviewed. Attention is then turned to the status and opportunities of Bayesian statistics for pharmaceutical drugs and biologicals. There are harbingers of change in the wind and these will be reviewed. Finally the challenges and the future of Bayesian statistics in the regulatory environment will be tackled.

Bayesian Dose-finding Designs for Modern Drug Development,
Yuan Ji, Professor of Biostatistics, Department of Public Health Sciences, The University of Chicago

In this talk, I will introduce a variety and representative Bayesian designs for dose-finding trials. The topics to be covered include classical DLT-based dose-finding designs, designs with delayed toxicity using time-to-event endpoints, and designs for combination dose-finding trial. The key takeaways will be the illustration of Bayesian modeling and inference for dose-finding designs that utilize the concept of probability intervals and decision making. Examples and software packages will be provided to illustrate various methodologies.

Bayesian Dynamic Borrowing for Partial Extrapolation and Bridging Studies: Methods and Case Studies,
Nicky Best, Head, Advanced Biostatistics and Data Analytics Centre of Excellence, GSK

In settings such as paediatric drug development or multi-regional clinical trials & bridging studies, a substantial body of evidence typically already exists regarding the drug efficacy and safety in other populations (e.g. adults or other regions). Bayesian dynamic borrowing methods offer a scientifically rigorous way to formally leverage this existing knowledge to better inform drug development and regulatory decision-making in these settings. In this webinar, I will introduce the key concepts and methodological details of Bayesian dynamic borrowing, focusing on the robust mixture prior method introduced by Schmidli et al (2014). Case studies will be presented to illustrate the application of this approach to real examples in a regulatory setting, and the benefits and challenges will be discussed.

Key Takeaways:

  • Bayesian dynamic borrowing methods offer a scientifically rigorous way to formally leverage existing knowledge to better inform drug development and regulatory decision-making.
  • Bayesian dynamic borrowing has been successfully used as a post-hoc analysis to support regulatory approval of a paediatric indication expansion for an approved adult treatment for systemic lupus erythematosus.
  • Use of Bayesian borrowing designs as a pre-specified primary analysis requires clear communication and justification for the choice of prior and the study decision criteria, and consideration of a broad range of operating characteristics in addition to traditional frequentist control of type 1 error, in order to evaluate the benefits and risks of the proposed design.


Statistical Design and Conduct of Platform Trials, Jason Connor, President & Lead Statistical Scientist, ConfluenceStat

The talk will focus on the statistical design and conduct of Platform Trials. These are large trials, usually designing under a Master Protocol, that focus on a disease rather than on a therapy. The trial may study multiple drugs or devices simultaneously with the intention of being a perpetual design to serially test therapeutics for a specific disease area.

Bayesian Models for Precision Oncology Clinical Trials,
Peter Mueller, Professor, Department of Mathematics and the Department of Statistics & Data Science, The University of Texas at Austin

We propose an adaptive Bayesian clinical trial design for patient allocation and subpopulation identification. We start with a decision
theoretic approach, including a utility function and a probability model across all possible subpopulation models. The main features of the proposed design and population finding methods are the use of a
flexible non-parametric Bayesian survival regression based on a random
covariate-dependent partition of patients, and decisions based on a
flexible utility function that reflects the requirement of the clinicians appropriately and realistically, and the adaptive allocation of patients to their superior treatments.

Recent Development on Bayesian Clinical Trial Designs Using Historical Data,
Ming-Hui Chen, Professor and Head of the Department of Statistics, The University of Connecticut

This webinar starts with a brief overview of variations of the power prior for borrowing historical information and the recent development of the Bayesian methodology for the design of clinical trials. A general Bayesian methodology within the Bayesian decision rule framework for the design of clinical trials with a focus on controlling type I error and power will be introduced. Various special cases, including the posterior probability approach, the Bayesian factor approach, and the conditional borrowing approach, are discussed. In addition, various fitting priors such as power priors and hierarchical priors are constructed for the incorporation of historical data. Various properties of the Bayesian methods are examined and simulation-based computational algorithms are discussed. The Bayesian methods are then applied to the design of a non-inferiority medical device clinical trial with historical data from previous trials to demonstrate superiority of the Bayesian methods in sample size reduction.

Key takeaways:

  • What is the power prior and how to determine the amount of historical data being borrowed?
  • What is Bayesian sample size determination?
  • How to control Bayesian type I error and increase Bayesian power?
  • How to reduce sample size?