Abstract
BackgroundThe main purpose of dose-finding studies in Phase I trial is to estimate maximum tolerated dose (MTD), which is the maximum test dose that can be assigned with an acceptable level of toxicity. Existing methods developed for single-agent dose-finding assume that the dose-toxicity relationship follows a specific parametric potency curve. This assumption may lead to bias and unsafe dose escalations due to the misspecification of parametric curve.MethodsThis paper relaxes the parametric assumption of dose-toxicity relationship by imposing a Dirichlet process prior on unknown dose-toxicity curve. A hybrid algorithm combining the Gibbs sampler and adaptive rejection Metropolis sampling (ARMS) algorithm is developed to estimate the dose-toxicity curve, and a two-stage Bayesian nonparametric adaptive design is presented to estimate MTD.ResultsFor comparison, we consider two classical continual reassessment methods (CRMs) (i.e., logistic and power models). Numerical results show the flexibility of the proposed method for single-agent dose-finding trials, and the proposed method behaves better than two classical CRMs under our considered scenarios.ConclusionsThe proposed dose-finding procedure is model-free and robust, and behaves satisfactorily even in small sample cases.
Highlights
The main purpose of dose-finding studies in Phase I trial is to estimate maximum tolerated dose (MTD), which is the maximum test dose that can be assigned with an acceptable level of toxicity
The main merits of the proposed method include that (i) the assumption of a specific dose-toxicity curve in continual reassessment method (CRM) is not necessary; (ii) there are only two hyperparameters in the specified Dirichlet process (DP) prior; (iii) there is more information that can be used to estimate toxicity probabilities and MTD; (iv) the escalation or deescalation of the current dose to the adjacent dose is only implemented once, the highest toxicity dose can be adaptively reached; (v) doses with relatively high toxicity probability may have no chance to be assigned to patients, which guarantees the safety of patients
Fourth, when the parameters in the DP prior are fixed, the proposed Bayesian nonparametric continual reassessment method (NCRM) behaves better than traditional model-based CRM; but when the parameters in the DP prior are unknown, the selection of the weight α has a positive effect on the selection probabilities and the number of patients treated at the MTD, while the selection of parameters in the DP prior has little effect on the total number of toxicities observed
Summary
The main purpose of dose-finding studies in Phase I trial is to estimate maximum tolerated dose (MTD), which is the maximum test dose that can be assigned with an acceptable level of toxicity. Existing methods developed for single-agent dose-finding assume that the dose-toxicity relationship follows a specific parametric potency curve. This assumption may lead to bias and unsafe dose escalations due to the misspecification of parametric curve. Dose-finding designs for phase I trials have been widely discussed over the past two decades. Many methods have been proposed to identify maximum tolerated dose (MTD) in single-agent dose-finding clinical trials. Among the model-based methods, the continual reassessment method (CRM) proposed by O’Quigley et al [1] is a quite popular dose-finding approach. See Whitehead and Brunier [2] for Bayesian decision-theoretic approach, Piantadosi et al [3] for a modified CRM, Heyd and Carlin [4] for an adaptive
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