TITE‐gBOIN: Time‐to‐event Bayesian optimal interval design to accelerate dose‐finding accounting for toxicity grades

Abstract

The new therapeutic agents, such as molecular targeted agents and immunooncology therapies, appear more likely to induce multiple toxicities at different grades than dose-limiting toxicities defined in traditional dose-finding trials. In addition, it is often challenging to make adaptive decisions on dose escalation and de-escalation on time because of the fast accrual rate and/or the late-onset toxicity outcomes, causing the potential suspension of the enrollment and the delay of the trials. To address these issues, we propose a time-to-event Bayesian optimal interval design to accelerate the dose-finding process utilizing toxicity grades based on both cumulative and pending toxicity outcomes. The proposed design, named "TITE-gBOIN" design, is a nonparametric and modelassisted design and has the virtues of robustness, simplicity and straightforward to implement in actual oncology dose-finding trials. A simulation study shows that the TITE-gBOIN design has a higher probability of selecting the MTDs correctly and allocating more patients to the MTDs across various realistic settings while reducing the trial duration significantly, therefore can accelerate early-stage dose-finding trials.

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

“…It reduces trial duration compared to designs that do not allow sequential dose allocation. The design performance may depend on the appropriate specification of the quasi-Bernoulli endpoint such as the normalized ETS [ 26 ]. A time-consuming collaboration between clinicians and biostatisticians is required to accurately derive the quasi-Bernoulli endpoint utilizing the weight for each toxicity grade and the drug-specific toxicity profiles [ 26 ].…”

“… Software to implement the TITE-gBOIN design is available at https://github.com/qingxiaa/titegboin . [ 26 ] BOIN-ET BOIN-ET design, a phase I/II design, is an extension of the BOIN design that utilizes both binary efficacy and toxicity outcomes in determining the OBD In general, the BOIN-ET design selects the OBD more accurately and puts a higher average number of patients at the OBD than the model-based TC and SHH designs [ 27 ]. It is simpler and easier to implement, is safer, and provides much better overdosing control than these designs [ 27 ].…”

“…The TITE-gBOIN is a non-parametric, model-assisted design that is an extension of the gBOIN design that accounts for toxicity grades based on both cumulative and pending numeric toxicity scores [ 26 ]. Although the reference by Takeda et al [ 26 ] focuses on quasi-Bernoulli toxicity endpoints, the TITE-gBOIN design proposed by the authors can be extended to continuous endpoints.…”

“…The design performance may depend on the appropriate specification of the quasi-Bernoulli endpoint such as the normalized equivalent toxicity score (ETS) [ 26 ]. A time-consuming collaboration between clinicians and biostatisticians is required to accurately derive the quasi-Bernoulli endpoint utilizing the weight for each toxicity grade and the drug-specific toxicity profiles [ 26 ]. The clinician needs to consider the clinical meaning while assigning the quasi-Bernoulli endpoint to a given grade of toxicity, while the biostatistician needs to evaluate the robustness of the quasi-Bernoulli endpoint being considered via simulations [ 26 ].…”

“…The TITE-gBOIN is a non-parametric, model-assisted design that is an extension of the gBOIN design that accounts for toxicity grades based on both cumulative and pending numeric toxicity scores [ 26 ]. Although the reference by Takeda et al [ 26 ] focuses on quasi-Bernoulli toxicity endpoints, the TITE-gBOIN design proposed by the authors can be extended to continuous endpoints. The TITE-gBOIN design minimizes the posterior probability of incorrect dose allocation for patients, while allowing sequential enrollment even with pending toxicity assessment for some patients.…”