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Dose-finding clinical trials in oncology aim to determine the maximumtolerated dose (MTD) of a new drug, generally defined by the proportion ofpatients with short-term dose-limiting toxicities (DLTs). Model-basedapproaches for such phase I oncology trials have been widely designed and aremostly restricted to the DLTs occurring during the first cycle of treatment,although patients continue to receive treatment for multiple cycles. We aim toestimate the probability of DLTs over sequences of treatment cycles via aBayesian cumulative modeling approach, where the probability of DLT is modeledtaking into account the cumulative effect of the administered drug and the DLTcycle of occurrence. We propose a design, called DICE (Dose-fIndingCumulativE), for dose escalation and de-escalation according to previouslyobserved toxicities, which aims at finding the MTD sequence (MTS). We performedan extensive simulation study comparing this approach to the time-to-eventcontinual reassessment method (TITE-CRM) and to a benchmark. In general, ourapproach achieved a better or comparable percentage of correct MTS selection.Moreover, we investigated the DICE prediction ability.

Bayesian model for early dose-finding in phase I trials with multiple treatment courses