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Adaptive Dose Finding Using Toxicity Probability Intervals

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.

Rule-based methods combine toxicity data with decision rules to determine whether a given cohort of patients should receive a higher or lower dose than the one before. Such methods are easy to implement since doses are assigned to a given cohort in a fairly intuitive manner using decision rules that are simple to follow. By contrast, model-based methods combine data with stasticial modeling, thereby requiring more mathematical calculation and specialized software like East 6.3. However, such model-based methods are better able to capture relevant toxicity information using all of the data available. 

In a recent presentation before the Cytel East Users Group Meeting, Neby Bekele of Gilead Sciences presented an adaptive dose-finding method which aims to combine the most useful elements of the rule-based and model-based methods. Toxicity probability models, according to Bekele, utilize decision-rules which lead to the same decisions as the complex decision-theoretic frameworks favored by model-based methods. As a result toxicity probability models like the mTPI provide a suitable middle ground between rule-based and model-based methods for dose finding.

For a full account of Neby Bekele’s argument, please click on the presentation slides below:

View Slides

Related Items of Interest

Powering Oncology Trials for Success: Adaptive Designs in East

Adaptive Designs for Evidence Based Oncology 

Bayesian Trial Designs are Safe, Efficient and Flexible: A Review of Published Phase 1 Studies

Adaptive Dose Finding Using Toxicity Probability Intervals



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