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In oncology dose-finding trials, due to staggered enrollment, it might bedesirable to make dose-assignment decisions in real-time in the presence ofpending toxicity outcomes, for example, when the dose-limiting toxicity islate-onset. Patients' time-to-event information may be utilized to facilitatesuch decisions. We review statistical frameworks for time-to-event modeling indose-finding trials and summarize existing designs into two classes: TITEdesigns and POD designs. TITE designs are based on inference on toxicityprobabilities, while POD designs are based on inference on dose-findingdecisions. These two classes of designs contain existing individual designs asspecial cases and also give rise to new designs. We discuss and study thetheoretical properties of these designs, including large-sample convergenceproperties, coherence principles, and the underlying decision rules. Tofacilitate the use of these designs in practice, we introduce efficientcomputational algorithms and review common practical considerations, such assafety rules and suspension rules. Finally, the operating characteristics ofseveral designs are evaluated and compared through computer simulations.

Statistical Frameworks for Oncology Dose-Finding Designs with Late-Onset Toxicities: A Review