Sep 2, 2016 10:30:00 AM
Aug 31, 2016 1:11:57 PM
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.
Oct 28, 2014 9:30:00 AM
Phase 1 oncology trials typically use either rule-based methods or model-based methods to determine the most acceptable level of dose toxicity with which to move forward in Phase 2. This level of toxicity, called the maximum tolerated dose (or the MTD), is the dose which best balances the medical benefits of a higher dose with the risk of toxicity which comes from subjecting a patient to that same dose. Both rule-based methods and model-based methods determine the MTD by relying on small cohorts of patients who test a set of doses against their dose limiting toxicity.
Aug 8, 2014 9:00:00 AM
Photos leaked from JSM 2014 appear to show the Reverend Bayes partying with his entourage at the Cytel Cocktail Hour, held at Boston’s Seaport Hotel on August 4, 2014. Bayes is, of course, one of the stars of Cytel’s East 6.3, a modular software package driving drug development through high quality trial design and simulations. Bayes's contributions to the East ESCALATE and East PREDICT modules are posited to transform early phase dose-escalation and interim decision-making. The Reverend has also made scene-stealing cameo appearances in a number of projects with Cytel Consulting.
May 8, 2014 7:54:00 AM
( Editor's note: This post has been refreshed in December 2016)
Model based algorithms for Phase I dose-escalation have been in existence for nearly thirty years. Despite guarantees of increased statistical power and greater accuracy, there remains a clear preference for rule based algorithms amongst clinicians. The explanation for this is as old as the models themselves.