MUCE: Bayesian hierarchical modelling for the design and analysis of phase 1b multiple expansion cohort trials
Jiaying Lyu, Tianjian Zhou, Shijie Yuan, Wentian Guo, Yuan Ji
Abstract
Abstract We propose a multiple cohort expansion (MUCE) approach as a design or analysis method for phase 1b multiple expansion cohort trials, which are novel first-in-human studies conducted following phase 1a dose escalation. In a phase 1b expansion cohort trial, one or more doses of a new investigational drug identified from phase 1a are tested for initial antitumour activities in patients with different indications (cancer types and/or biomarker status). Each dose–indication combination defines an arm, and patients are enrolled in parallel cohorts to all the arms. The MUCE design is based on a class of Bayesian hierarchical models that adaptively borrow information across arms. Specifically, we employ a latent probit model that allows for different degrees of borrowing across doses and indications. Statistical inference is directly based on the posterior probability of each arm being efficacious, facilitating the decision making that decides which arm to select for further testing. The MUCE design also incorporates interim looks, based on which the nonpromising arms will be stopped early due to futility. Through simulation studies, we show that MUCE exhibits superior operating characteristics. We also compare the performance of MUCE with that of Simon’s two-stage design and some existing Bayesian designs for multiarm trials. To our knowledge, MUCE is the first Bayesian method for phase 1b expansion cohort trials with multiple doses and indications.