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Ranking Adaptive Dose-Finding Designs using Clinical Utility Functions

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Clinical utility functions provide Phase 2 trial sponsors with an intuitive metric by which to measure the quality of a selected dose. Such functions reveal the efficacy-to-tolerability ratio of doses under consideration, thereby enabling trials to move forward with doses that are highly effective and which have minimal side-effects. While this is arguably the most popular use of clinical utility functions, they can also help trial designers determine which design ought to be chosen for dose-finding studies. A design which consistently chooses doses with high clinical utility should instill greater confidence than those which often miss this critical target. 

In 2009, Ivanova et al proposed an adaptive design for dose-finding, constructed with the objective of maximizing the number of patients clustered towards the dose with optimal clinical utility [1].By assigning fewer patients to ineffective or intolerable doses, Ivanova’s Maximizing Design was also thought to yield improved information regarding dose-response. Recent simulations conducted by Cytel's Jim Bolognese demonstrate that Ivanova's Adaptive Maximizing Design displays an unusually high capacity to select the dose with highest clinical utility, while also providing improved information about dose-response. 

However, improved information quality is merely the tip of the iceberg. While the Maximizing Design was meant to increase the probability that the sponsored trial could move to a confirmatory trial with an accurate dose, it provides obvious benefits for patients. A design which assigns the majority of patients to a dose with high clinical utility ensures that the majority of patients receive a treatment high enough to be effective while not so high as to generate unwanted side-effects. The Ivanova Maximing Design not only minimizes the number of patients assigned to ineffective or intolerable doses, it also minimizes the number of patients assigned to effective and tolerable doses that are still suboptimal. Jim's results indicate that the Maximizing Design may in fact maximize a patient's chance to receive the dose with the highest clinical utility. 

For more information see Jim's slides below. 

View Slides

Related Items of Interest

Ivanova A, Liu K, Snyder E, Snavely D. An adaptive design for
identifying the dose with the best efficacy/tolerability profile
with application application to a crossover crossover dose‐finding finding study. Statist Statist.Med. 2009; 28:2941‐2951

Ivanova A, Xiao C, Tymofyeyev Y. Two-Stage designs for Phase 2 dose‐finding trials. Statist. Med. 2012; 31:2872–2881

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