Abstract
This short communication presents a first in human Bayesian Optimal Interval design case study. The study design and associated operating characteristics are discussed, together with study amendments proposed whilst the study was ongoing. Simulations investigating the impact of the amendments on the operating characteristics of the study design are presented. Lessons learnt from the case study, including providing practical advice when designing smarter early phase oncology trials to identify the maximum tolerate dose are also summarised. It is argued that model-assisted designs are simple to implement, flexible and perform significantly better than the commonly used "3 + 3" design, and thus should become the go to design for statisticians when limited information is known about the dose toxicity curve.
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