An Adaptive Trial Design to Optimize Dose Schedule Regimes with Delayed Outcomes

• Activity Number: 579 - 30 Years Journey of Bayesian Adaptive Designs: From Bayesian Monitoring to Dynamic Treatment Regime — in Honor of Dr. Peter Thall’s 70th Birthday
• Type: Topic Contributed
• Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Ruitao LIN* and Ying Yuan and Peter F. Thall
• Companies: The University of Texas MD Anderson Cancer Center and The University of Texas MD Anderson Cancer Center and M.D. Anderson Cancer Center
• Keywords: Adaptive design; Bayesian methods; Dose finding; Delayed outcomes; Adaptive randomization; Utility

Abstract

A two?stage phase I?II clinical trial design is proposed to optimize dose?schedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity?efficacy trade?off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decision?making is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood?based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy?toxicity trade?off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the po