Bayesian adaptive phase II screening design for combination trials

Abstract

Background Trials of combination therapies for the treatment of cancer are playing an increasingly important role in the battle against this disease. To more efficiently handle the large number of combination therapies that must be tested, we propose a novel Bayesian phase II adaptive screening design to simultaneously select among possible treatment combinations involving multiple agents. Methods Our design is based on formulating the selection procedure as a Bayesian hypothesis testing problem in which the superiority of each treatment combination is equated to a single hypothesis. During the trial conduct, we use the current values of the posterior probabilities of all hypotheses to adaptively allocate patients to treatment combinations. Results Simulation studies show that the proposed design substantially outperforms the conventional multiarm balanced factorial trial design. The proposed design yields a significantly higher probability for selecting the best treatment while allocating substantially more patients to efficacious treatments. Limitations The proposed design is most appropriate for the trials combining multiple agents and screening out the efficacious combination to be further investigated. Conclusions The proposed Bayesian adaptive phase II screening design substantially outperformed the conventional complete factorial design. Our design allocates more patients to better treatments while providing higher power to identify the best treatment at the end of the trial.

Novel strategies in glioblastoma surgery aim at safe, supra-maximum resection in conjunction with local therapies

“…Screening through unconventional phase II trials in the framework of a working-party seems desirable[46]. Further testing can occur only in the event of an overwhelming survival benefit in a phase II trial.…”

“…Given the number of new agents, delivery variables, and all possible combinations, testing in classical clinical trials for the most powerful modality will be impractical. Screening through unconventional phase II trials in the framework of a working-party seems desirable [46] . Further testing can occur only in the event of an overwhelming survival benefit in a phase II trial.…”

Bayesian Sequential Learning for Clinical Trials of Multiple Correlated Medical Interventions

“…Multiarm, multistage (MAMS) trials use the first two approaches together (Sydes et al 2012, Wason and Jaki 2012, Jaki and Hampson 2016, Boeree et al 2017. Arms can represent individual treatments or combinations of options (Cai et al 2013), with or without controls (Magaret et al 2016), as well as the dose levels considered in phase II/III dose-finding trials (Huang et al 2015). Platform trials allow arms to dynamically enter and leave trials (Adaptive Platform Trials Coalition et al 2019).…”

Bayesian modeling and prediction of accrual in multi-regional clinical trials

“…As the bar for drug development rises, it presents a challenge to the industry in that the clinically meaningful improvement in therapeutic efficacy of a promising investigational treatment over an existing treatment is likely to be moderate in scale. This usually translates into substantial sample sizes in phase III clinical trials, noting that, unlike phase I/II trials, 1–3 adequate power is essential for phase III trials. In practice, these phase III trials easily require thousands of patients, if not more.…”