Abstract
Background/aims
In the conduct of phase I trials, the limited use of innovative model-based designs in practice has led to an introduction of a class of “model-assisted” designs with the aim of effectively balancing the trade-off between design simplicity and performance. Prior to the recent surge of these designs, methods that allocated patients to doses based on isotonic toxicity probability estimates were proposed. Like model-assisted methods, isotonic designs allow investigators to avoid difficulties associated with pre-trial parametric specifications of model-based designs. The aim of this work is to take a fresh look at an isotonic design in light of the current landscape of model-assisted methods.
Methods
The isotonic phase I method of Conaway, Dunbar, and Peddada was proposed in 2004 and has been regarded primarily as a design for dose-finding in drug combinations. It has largely been overlooked in the single-agent setting. Given its strong simulation performance in application to more complex dose-finding problems, such as drug combinations and patient heterogeneity, as well as the recent development of user-friendly software to accompany the method, we take a fresh look at this design and compare it to a current model-assisted method. We generated operating characteristics of the Conaway–Dunbar–Peddada method using a new web application developed for simulating and implementing the design and compared it to the recently proposed Keyboard design that is based on toxicity probability intervals.
Results
The Conaway–Dunbar–Peddada method has better performance in terms of accuracy of dose recommendation and safety in patient allocation in 17 of 20 scenarios considered. The Conaway–Dunbar–Peddada method also allocated fewer patients to doses above the maximum tolerated dose than the Keyboard method in many of scenarios studied. Overall, the performance of the Conaway–Dunbar–Peddada method is strong when compared to the Keyboard method, making it a viable simple alternative to the model-assisted methods developed in recent years.
Conclusion
The Conaway–Dunbar–Peddada method does not rely on the specification and fitting of a parametric model for the entire dose-toxicity curve to estimate toxicity probabilities as other model-based designs do. It relies on a similar set of pre-trial specifications to toxicity probability interval-based methods, yet unlike model-assisted methods, it is able to borrow information across all dose levels, increasing its efficiency. We hope this concise study of the Conaway–Dunbar–Peddada method, and the availability of user-friendly software, will augment its use in practice.
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