Enhancement of Bayesian optimal interval design by accounting for overdose and underdose errors trade-offs

Abstract

ABSTRACT Model-assisted designs, a new class of dose-finding designs for determining the maximum tolerated dose (MTD), model only the dose-limiting toxicity (DLT) data observed at the current dose based on a simple binomial model and offer the boundaries of DLT for the determination of dose escalation, retention, or de-escalation before beginning the trials. The boundaries for dose-escalation and de-escalation decisions are relevant to the operating characteristics of the design. The well-known model-assisted design, Bayesian Optimal Interval (BOIN), selects these boundaries to minimize the probability of incorrect decisions at each dose allocation but does not distinguish between overdose and underdose allocations caused by incorrect decisions when calculating the probability of incorrect decisions. Distinguishing between overdose and underdose based on the decision error in the BOIN design is expected to increase the accuracy of MTD determination. In this study, we extended the BOIN design to account for the decision probabilities of incorrect overdose and underdose allocations separately. To minimize the two probabilities simultaneously, we propose utilizing multiple objective optimizations and formulating an approach for determining the boundaries for dose escalation and de-escalation. Comprehensive simulation studies using fixed and randomly generated scenarios of DLT probability demonstrated that the proposed method is superior or comparable to existing interval designs, along with notably better operating characteristics of the proposed method.