The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.

Join us for the East Symposium and User Group Meeting

June 26, 2017

Since 2011 we have been celebrating Cytel's birthdays by holding an East Annual Symposium and User Group Meeting ( EUGM), alternating locations between Europe and the United States. Our keynote speakers have featured some of the industry's most eminent biostatistics thought leaders including Stuart Pocock, Dave DeMets, Chris Jennison, Sue Todd, and Franz Koenig.  Each annual event has been a great success with a  mix of scientific presentations, animated discussion, round tables and pre-conference software workshops. 

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Inside an Oncology Statistician's Toolkit

February 9, 2017

 

 

 In this blog, Adam Hamm, PhD, Director Biostatistics at Cytel shares some of the most important knowledge he uses in  his day to day work as a biostatistician working extensively in oncology research. Adam has broad experience with statistical analysis and methodology over all phases (I-IV) of development, in particular working in the oncology arena. 

 As a Director of Biostatistics at Cytel, I work on design, statistical analysis and reporting projects for a range of biotechnology and pharmaceutical sponsors. During my career, I’ve developed a particular focus on oncology trials, so in this blog I’ll share some insights into the  knowledge which I have found particularly vital  as a biostatistician working in this area. This knowledge spans specific statistical methodologies and understanding of the clinical issues across the phases of clinical development. The summary is not exhaustive, but provides a glimpse into the broad exposure which is needed for a biostatistician to develop a fully rounded understanding in the area. To learn more, read on...

 

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An Introduction to BLRM

August 31, 2016

Traditional rule-based approaches to dose escalation such as 3+3 are widely used in early clinical development. They can be appealing due to the simplicity of execution. However, estimates produced may be highly variable and the targeting of true Maximum Tolerated Dose may be poor.  Bayesian dose escalation approaches in early phase trials can offer an effective alternative to determining the maximum tolerable dose of a new drug more quickly, as well as ensuring that all of the information available to trial clinicians is taken into account so that the patients enrolled in the trial receive the best possible treatment.

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How does the T-Statistic stack up for finding MTD?

August 23, 2016

At the recent JSM meeting in Chicago, Cytel's Jim Bolognese presented the results of work he has conducted evaluating the T-Statistic ( or T-Stat) method for adaptive dose finding of MTD.  In this blog we'll provide a brief summary of Jim's findings, and share his slides with our blog readers.

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Webinar Replay: Single and Double Agent Dose Escalation Designs

July 21, 2016


Did you miss our webinar on Single and Dual Agent Dose escalation designs earlier in the year?  In this blog we have made the replay available for your review, and  also  take the opportunity to recap key reasons why you should consider a model based design for your dose escalation study.

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Bayesian Dose Escalation Designs for Late Onset Toxicity

November 10, 2015

Last week Cytel joined forces with Sanofi/Genzyme to devote a full day of workshops and talks related to modern methods in early phase oncology. 

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A Bayesian Industry Approach to Phase I Combination Trials in Oncology

November 18, 2014

Statisticians and scientists at Novartis have been at the forefront of developing a new method in early phase oncology trials called the BLRM. Many believe that the BLRM, (short for the Bayesian Logistic Regression Method,) allows for the construction of clinical trials that have the dual benefit of improving treatment for patients participating in the trials, and allowing the trial to complete in a more timely and efficient manner. 

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Adaptive Designs for Evidence Based Oncology: Insights from the Experts

September 4, 2014

Imagine if we were to count the number of possible reasons that investigators might have for monitoring a biomarker during a clinical trial, and multiply that number by the number of possible adaptive designs available for such investigation. We would naturally assume that whatever the number, it would be rather large. This poses an interesting question for a sponsor of an adaptive clinical trial. Are there any general principles for trial design that may be gleaned from these various possible scenarios?

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