U-PRO-CRM: designing patient-centred dose-finding trials with patient-reported outcomes.

Background: Determining the maximum tolerated dose (MTD) remains the primary objective for the majority of dose-finding oncology trials. Whilst MTD determination often relies upon clinicians to identify dose-limiting toxicities (DLTs) experienced by patients during the trial, research suggests that clinicians may underreport patient's adverse events. Therefore, contemporary practice may be exposed to recommending intolerable doses to patients for further investigation in subsequent trials. There is increasing interest in patients self-assessing their own symptoms using patient-reported outcomes (PROs) in dose-finding trials.

Design: We present Utility-PRO-Continual Reassessment Method (U-PRO-CRM), a novel trial design which simultaneously uses clinician-rated and patient-rated DLTs (Clinician-DLTs and Patient-DLTs, respectively) to make dose (de-)escalation decisions and to recommend an MTD. U-PRO-CRM contains the published PRO-CRM as a special case and provides greater flexibility to trade-off the rate of Patient-DLTs and Clinician-DLTs to find an optimal dose. We present simulation results for U-PRO-CRM.

Results: For specified trade-offs between Clinician-DLT and Patient-DLT rate, U-PRO-CRM outperforms the PRO-CRM design by identifying the true MTD more often. In the special case where U-PRO-CRM generalises to PRO-CRM, U-PRO-CRM performs as well as its published counterpart. U-PRO-CRM minimises the number of patients overdosed whilst maintaining a similar proportion of patients allocated to the true MTD.

Conclusions: By using a utility-based dose selection approach, U-PRO-CRM offers the flexibility to define a trade-off between the risk of patient-rated and clinician-rated DLTs for an optimal dose. Patient-centric dose-finding strategies, which integrate PROs, are poised to assume an ever more pivotal role in significantly advancing our understanding of treatment tolerability. This bears significant implications in shaping the future landscape of early-phase trials.