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Abstract

The common terminology criteria for adverse events by the National Cancer Institute has greatly facilitated the revolution of drug development and an increasing number of Phase I trials have started to collect multiple-grade toxicity endpoints. Appropriate and yet transparent Phase I statistical designs for multiple-grade toxicities are therefore in great needs. In this article, we propose a quasi-toxicity probability interval (qTPI) design that incorporates a quasi-continuous measure of the toxicity probability ( $q T P$ ) into the Bayesian theoretic framework of the interval based designs. Multiple-grade toxicity outcomes of each patient are mapped to $q T P$ according to a severity weight matrix. Dose–toxicity curve underlying the dosing decisions in the qTPI design is continuously updated using accumulating trial data. Numerical simulations investigating the operating characteristics of qTPI show that qTPI achieved better safety, accuracy and reliability compared to designs that rely on binary toxicity data. Furthermore, parameter elicitation in qTPI is simple and does not involve multiple hypothetical cohorts specification. Finally, a hypothetical soft tissue sarcoma trial with six toxicity types and grade 0 to grade 4 severity grades is illustrated with patient-by-patient dose allocation under the qTPI design.

Keywords

Multiple-grade toxicity phase I trial design Bayesian adaptive design interval-based design

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qTPI: A quasi-toxicity probability interval design for phase I trials with multiple-grade toxicities

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