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A Bayesian model‐free approach to combination therapy phase I trials using censored time‐to‐toxicity data

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Many treatment regimens for cancers use several different drugs or treatment types. For combinations of one or more novel treatments, finding a dose/therapy combination that has a controlled and acceptable risk of severe toxicity presents several challenges. In particular, for trials involving radiotherapy, late‐onset treatment‐related toxicities are a possibility. This means patients need to be observed for a longer-than-usual period to assess for severe toxicities that could lead to one or more treatments being de-escalated. When a current trial patient is being observed for severe toxicity and one or more new patients are recruited to trial, waiting until the current patient has completed their follow-up could lead to an unreasonably long trial duration and excessive costs. Also, this would delay the start of treatment for the patients-in-waiting.

In this talk, I present an adaptation of the Product of Independent beta Probabilities Escalation (PIPE) design, an approach for dual-agent phase I trials that has already been implemented in practice since its publication in 2015. The adaptation uses censored time‐to‐toxicity outcomes for patients still in follow-up to aid dose escalation decisions for future trial patients. I will compare the original PIPE approach to this time-to-toxicity adaptation, and briefly discuss possible extensions to these methods to deal with more complex clinical trials.

This talk is part of the Cambridge Statistics Discussion Group (CSDG) series.

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