The New England Journal of Statistics in Data Science

Phase I oncology trials aim to identify a safe dose—often the maximum tolerated dose (MTD)—for subsequent studies. Conventional designs focus on population-level toxicity modeling, with recent attention on leveraging pharmacokinetic (PK) data to improve dose selection. We propose the Precision Dose-Finding (PDF) design, a novel Bayesian phase I framework that integrates individual patient PK profiles into the dose-finding process. By incorporating patient-specific PK parameters (such as volume of distribution ${V_{i}}$ and elimination rate ${k_{i}}$), PDF models toxicity risk at the individual level, in contrast to traditional methods that ignore inter-patient variability. The trial is structured in two stages: an initial training stage to update model parameters using cohort-based dose escalation, and a subsequent test stage in which doses for new patients are chosen based on each patient’s own PK-predicted toxicity probability. This two-stage approach enables truly personalized dose assignment while maintaining rigorous safety oversight. Extensive simulation studies demonstrate the feasibility of PDF and suggest that it provides improved safety and dosing precision relative to the continual reassessment method (CRM). The PDF design thus offers a refined dose-finding strategy that tailors the MTD to individual patients, aligning phase I trials with the ideals of precision medicine.

In the last two decades, single-arm trials (SATs) have been effectively used to study anticancer therapies in well-defined patient populations using durable response rates as an objective and interpretable study endpoints. With a growing trend of regulatory accelerated approval (AA) requiring randomized controlled trials (RCTs), some confusions have arisen about the roles of SATs in AA. This review is intended to elucidate necessary and desirable conditions under which an SAT may be considered appropriate for AA. Specifically, the paper describes (1) two necessary conditions for designing an SAT, (2) eight desirable conditions that help either optimize the study design and doses or interpret the study results, and (3) three additional considerations for construction of estimands, adaptive designs, and timely communication with relevant regulatory agencies. Three examples are presented to demonstrate how SATs can or cannot provide sufficient evidence to support regulatory decision. Conditions and considerations presented in this review may serve as a set of references for sponsors considering SATs to support regulatory approval of anticancer drugs.