Bayesian Optimal Interval Design with Multiple Toxicity Constraints

Abstract

Most phase I dose-finding trials are conducted based on a single binary toxicity outcome to investigate the safety of new drugs. In many situations, however, it is important to distinguish between various toxicity types and different toxicity grades. By minimizing the maximum joint probability of incorrect decisions, we extend the Bayesian optimal interval (BOIN) design to control multiple toxicity outcomes at prespecified levels. The developed multiple-toxicity BOIN design can handle equally important, unequally important as well as nested toxicity outcomes. Interestingly, we find that the optimal interval boundaries with non-nested toxicity outcomes under the proposed method coincide with those under the standard single-toxicity BOIN design by treating the multiple toxicity outcomes marginally. We establish several desirable properties for the proposed interval design. We additionally extend our design to address trials with combined drugs. The finite-sample performance of the proposed methods is assessed according to extensive simulation studies and is compared with those of existing methods. Simulation results reveal that, our methods are as accurate and efficient as the more complicated model-based methods, but are more robust and much easier to implement.

An overview of the BOIN design and its current extensions for novel early-phase oncology trials

“…The MT-BOIN (multiple toxicity BOIN) design(s) is an extension of the BOIN design that considers different toxicity types and grades [ 15 ].…”

“…Here, for the dosing algorithm, we focus on one of the cases that MT-BOIN considers, namely that of two non-nested toxicity outcomes 3 where the two toxicities do not depend on each other. The specific steps of the MT-BOIN design to identify the MTD with two non-nested toxicity outcomes Y 1 and Y 2 are as follows [ 15 ]: Enroll the first cohort of patients at the lowest dose or starting dose. The number of patients who have experienced toxicity Y l is y lj out of n j treated patients.…”

Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens

“…While a binary toxicity outcome is convenient in the context of small sample size, various types of DLT might not be identified as equally important 10 . Several authors have proposed to define a continuous toxicity score to differentiate the types of toxicity 10‐12 and to define multiple toxicity constraints on the toxicity types and grades to define the MTD 13‐16 …”

“…10 Several authors have proposed to define a continuous toxicity score to differentiate the types of toxicity [10][11][12] and to define multiple toxicity constraints on the toxicity types and grades to define the MTD. [13][14][15][16] Several methods have been developed to consider more complex schedules of administration. Dose-finding designs have been proposed to evaluate the dose-schedule combination in a two-dimensional framework.…”

On the relative efficiency of model-assisted designs: a conditional approach

“…Yuan et al (2018) 14 developed the time-to-event BOIN design to handle late-onset toxicities or fast patient accrual. Lin (2018) 15 generalized the BOIN design to handle multiple toxicities. Lin and Yin (2017), 16 Zhang and Yuan (2016), 17 and Pan, et al (2018) 18 extended the BOIN and keyboard designs to drug-combination trials.…”