The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
Bjoern Bornkamp, Statistical Methodologist at Novartis and Jose Pinheiro, Senior Director, Johnson & Johnson provided their insights on adaptive designs for dose finding in Cytel’s latest webinar. Finding the right dose in Phase 2 gives a potential new therapy its best chance to demonstrate efficacy during Phase 3. A well-executed dose-ranging trial therefore has the potential to alter the course of the entire clinical development program. This webinar demonstrates how adaptive and Bayesian techniques can be implemented for optimal dose-finding.
This two-part blog series will provide a summary of the webinar. In this first part, get key highlights from the presentation by Jose Pinheiro on the need to conduct dose finding Phase 2 studies, dose selection comparisons and the use of MCP-Mod for dose finding. Click the button to access the webinar recording and download the presentation slides
Making the Most of Your Data II: Optimizing Clinical Information in Trial Design and Implementation Using Bayesian Methods
While there is increasing optimism about the discovery of a COVID-19 vaccine, one of the less talked about aspects of such vaccines development are the lessons that can be used in other therapeutic areas. After all, COVID-19 vaccines development has uncovered numerous ways to design and execute trials within shorter time-frames and with less data.
One theme that has emerged consistently is the need to optimize the use of clinical information available, an endeavor well-supported by Bayesian methods.
Cytel is conducting a webinar series on complex innovative trial designs. Dr. Thomas Burnett, Senior Research Associate in Medical and Pharmaceutical Statistics at Lancaster University, joined us as the presenter in the latest webinar from this series. In this webinar, “Adaptive Enrichment Designs in Clinical Development”, Dr. Burnett provides us a brief introduction to population enrichment and explains where it fits in clinical trials. He offers his insights on the topics of hypothesis testing and decision making, which is a key component of adaptive designs. You can also learn about a real-world case study (TAPPAS Trial) where this approach was used. Continue reading this blog for highlights from the webinar.
Watch the webinar recording and download the slides by clicking the button.
Read an interview with Dr. Thomas Burnett on adaptive enrichment.
Cytel brings to you a new blog series on technology and Bayesian decision-making by Pantelis Vlachos, Principal/Strategic Consultant for Cytel. In his inaugural post Pantelis walks us through the features and benefits of our new offering, East Alloy™. East Alloy™ is a web-based extension of East for clinical trial design that blends the pace of SaaS delivery, the ease of use and robustness of Cytel software, and the velocity of cloud-based computing. Gain some behind-the-scenes insights into the development of this new module and understand how your company can leverage East Alloy to conduct computationally intensive designs with ease, confidence, and speed.
Last week, Cytel conducted its third webinar in the new introductory webinar series on Complex Innovative Trial Designs. Our speaker, Dr. Satrajit Roychoudhury is a Senior Director, Statistical Research and Data Science Center at Pfizer. In this webinar, Dr. Roychoudhury gets into the basics of phase I designs in oncology trials, explains the caveats of frequently used traditional designs and provides insights on how implementing a model-based approach can enable a better statistical inference and decision-making. You can watch the replay of the webinar and access the slides by clicking on the button.
We also had the privilege to interview Dr. Satrajit Roychoudhury. Read our blog where he talks about his interest in statistics, explains the concept of Bayesian model-based approaches and their importance in oncology trials.
One of the revelations of the COVID-19 pandemic is that the flexibility and potential of Bayesian designs goes far beyond the benefits connected to informed priors. Rather a number of other benefits to Bayesian designs are sometimes under-appreciated. The importance of using Bayesian methods to choose composite endpoints, for example, particularly in longitudinal studies, can be overlooked when considering Bayesian and Frequentist options.
Cytel statisticians reflected on these benefits during a recent panel discussion.
Significance of Bayesian Model-Based Approaches in Oncology Trials: An Interview with Dr. Satrajit Roychoudhury
On June 17, 2020, Cytel is conducting a webinar with Dr. Satrajit Roychoudhury, Senior Director, Statistical Research and Data Science Center, Pfizer. Dr. Roychoudhury will be talking about practical model-based approaches for phase I oncology trials. This webinar is a part of Cytel’s “Introduction to Complex Innovative Trial Designs” webinar series. You can register by clicking on the button below.
In this blog, we bring to you an insightful interview with Dr. Satrajit Roychoudhury where he talks to us about his interest in statistics, explains the concept of Bayesian model-based approaches and their importance in oncology trials.
A recent Cytel panel led by Vice President of Strategic Consulting Natalia Muhlemann evaluated the role that Bayesian methods played in development of a COVID-19 vaccine. The wide-ranging discussion covered the structure and utility of platform trials and the role of master protocols in infectious disease vaccines development, but also addressed the importance of adaptive Bayesian methods in the search for COVID-19 therapies.
In our previous blog, we spoke with Alind Gupta, who works as a Machine Learning Researcher at Cytel in Canada. The interview gives you a deep dive into black-box models and transparent machine learning, and how the latter is becoming more important in clinical research today.
On March 21, Cytel conducted a webinar with Alind on, “Transparent Machine Learning in Oncology”. Alind presented our continuing work in immuno-oncology using Bayesian network models for predicting safety and survival outcomes, extrapolating from limited follow-up data and validating with external real-world data for key subgroups. Continue reading for key highlights from the webinar.
Register now to get free access to webinar slides and recording.