Optimal Adaptive Designs for Dose Finding in Early Phase Clinical Trials

Abstract

A method of designing early clinical trials is developed for finding an optimum dose level of a new drug to be recommended for use in later phases. During the trial, the efficacious doses are allocated to the patients more often and those with a high probability of toxicity are less likely to be chosen. The method proposed is adaptive in the sense that the statistical models are updated after the data from each cohort of patients are collected and the dose level is adjusted at each stage based on the current data. Two classes of designs are presented. Although both are for efficacy and toxicity responses, one of them also considers pharmacokinetic information. The dose optimisation criteria are based on the probability of success and on the determinant of the Fisher information matrix for estimation of the dose-response parameters. They can be constrained by both acceptable levels of the probability of toxicity and desirable levels of the area under the concentration curve or the maximum concentration. The method presented is general and can be applied to various dose-response and pharmacokinetic models. To illustrate the methodology, it is applied to two different classes of models. In both cases, the pharmacokinetic model incorporates the population variability by making appropriate assumptions about the model parameters, while the dose responses are assumed to be either trinomial or bivariate binomial. Various design properties of the method are examined by simulation studies. Efficiency measures and the sensitivity of the designs to the assumed prior parameter values are presented. All of the computations are conducted in R, where the D- v optimal sampling time points are obtained by using the package PFIM. The results show that the proposed adaptive method works well and could be appropriate as a seamless phase IB/IIA trial design.