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

Abstract

In oncology dose-finding trials, due to staggered enrollment, it might be desirable to make dose-assignment decisions in real-time in the presence of pending toxicity outcomes, for example, when the dose-limiting toxicity is late-onset. Patients' time-to-event information may be utilized to facilitate such decisions. We review statistical frameworks for time-to-event modeling in dose-finding trials and summarize existing designs into two classes: TITE designs and POD designs. TITE designs are based on inference on toxicity probabilities, while POD designs are based on inference on dose-finding decisions. These two classes of designs contain existing individual designs as special cases and also give rise to new designs. We discuss and study the theoretical properties of these designs, including large-sample convergence properties, coherence principles, and the underlying decision rules. To facilitate the use of these designs in practice, we introduce efficient computational algorithms and review common practical considerations, such as safety rules and suspension rules. Finally, the operating characteristics of several designs are evaluated and compared through computer simulations.