Surv‐CRM‐12: A Bayesian phase I/II survival CRM for right‐censored toxicity endpoints with competing disease progression

Abstract

The growing interest in new classes of anti-cancer agents, such as molecularly-targeted therapies and immunotherapies with modes of action different from those of cytotoxic chemotherapies, has changed the dose-finding paradigm. In this setting, the observation of late-onset toxicity endpoints may be precluded by treatment and trial discontinuation due to disease progression, defining a competing event to toxicity. Trial designs where dose-finding is modeled in the framework of a survival competing risks model appear particularly well-suited. We aim to provide a phase I/II dose-finding design that allows dose-limiting toxicity (DLT) outcomes to be delayed or unobserved due to competing progression within the possibly long observation window. The proposed design named the Survival-continual reassessment method-12, uses survival models for right-censored DLT and progression endpoints. In this competing risks framework, cause-specific hazards for DLT and progression-free of DLT were considered, with model parameters estimated using Bayesian inference. It aims to identify the optimal dose (OD), by minimizing the cumulative incidence of disease progression, given an acceptable toxicity threshold. In a simulation study, design operating characteristics were evaluated and compared to the TITE-BOIN-ET design and a nonparametric benchmark approach. The performance of the proposed method was consistent with the complexity of scenarios as assessed by the nonparametric benchmark. We found that the proposed design presents satisfying operating characteristics in selecting the OD and safety.

Adaptive phase I–II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies

“…Several phase I-II designs have been proposed, which use the competing risks and semi-competing risks models developed for survival analysis to resolve this issue. [70][71][72][73] Through the integration of phase I and II trials, a phase I-II trial requires a larger sample size than a traditional phase I trial, resulting in increased costs and longer trial durations. As per the FDA guidelines, no general mathematical formulas are available for determining the sample size of phase I-II designs.…”

“…Several phase I–II designs have been proposed, which use the competing risks and semi-competing risks models developed for survival analysis to resolve this issue. 70–73…”

Dose optimization for cancer treatments with considerations for late-onset toxicities

“…[48][49][50][51][52][53] Proposals are numerous, and this is not an exhaustive list. The proposals differ in their definition of the optimal dose(s) which depend on the design's objective: for example, maximizing the efficacy criterion while satisfying a toxicity constraint [48][49][50][54][55][56] or optimizing an efficacy-toxicity trade-off or utility measure. 52,[57][58][59][60] Given the small sample sizes and short follow-up periods in a dose-finding trial, it is important to continue the evaluation of efficacy and longer-term tolerability in the dose-optimization trial using selection designs and dose ranging designs in the intended patient population.…”

“…48–53 Proposals are numerous, and this is not an exhaustive list. The proposals differ in their definition of the optimal dose(s) which depend on the design’s objective: for example, maximizing the efficacy criterion while satisfying a toxicity constraint 48–50,54–56 or optimizing an efficacy–toxicity trade-off or utility measure. 52,57–60…”