Determining doses for backfill cohorts based on patient-reported outcome

Abstract

Abstract Background Incorporating backfill cohorts in phase I oncology trials is a recently developed strategy for dose optimization. However, the efficacy assessment window is long in general, causing a lag in identifying ineffective doses and more patients being backfilled to those doses. There is necessity to investigate how to use patient-reported outcomes (PRO) to determine doses for backfill cohorts. Methods We propose a unified Bayesian design framework, called ‘Backfill-QoL’, to utilize patient-reported quality of life (QoL) data into phase I oncology trials with backfill cohorts, including methods for trial monitoring, algorithm for dose-finding, and criteria for dose selection. Simulation studies and sensitivity analyses are conducted to evaluate the proposed Backfill-QoL design. Results The simulation studies demonstrate that the Backfill-QoL design is more efficient than traditional dose-expansion strategy, and fewer patients would be allocated to doses with unacceptable QoL profiles. A user-friendly Windows desktop application is developed and freely available for implementing the proposed design. Conclusions The Backfill-QoL design enables continuous monitoring of safety, efficacy and QoL outcomes, and the recommended phase II dose (RP2D) can be identified in a more patient-centered perspective.

Keywords

• Backfill cohorts
• Quality of life
• Phase I clinical trials
• Dose optimization