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University of Cambridge > Talks.cam > Cambridge Statistics Discussion Group (CSDG) > A Bayesian model‐free approach to combination therapy phase I trials using censored time‐to‐toxicity data
A Bayesian model‐free approach to combination therapy phase I trials using censored time‐to‐toxicity dataAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Peter Watson. 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. This talk is included in these lists:
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