Two-stage adaptive designs in early phase clinical trials

Abstract

The primary goal of clinical trials is to collect enough scientific evidence for a new intervention. Despite the widespread use of equal randomization in clinical trials, response-adaptive randomization has attracted considerable interest in terms of ethical concerns. In this thesis, delayed response problems and innovative designs for cytostatic agents in oncology clinical trials are studied.

There is typically a prerun of equal randomization before the implementation of response-adaptive randomization, while it is often not clear how many subjects are needed in this prephase, and in practice an arbitrary number of patients are allocated in this equal randomization stage. In addition, real-time response-adaptive randomization often requires patient response to be immediately available after the treatment, while clinical response, such as tumor shrinkage, may take a relatively long period of time to exhibit. In the first part of the thesis, a nonparametric fractional model and a parametric optimal allocation scheme are developed to tackle the common problem caused by delayed response. In addition, a two-stage procedure to achieve a balance between power and the number of responders is investigated, which is equipped with a likelihood ratio test before skewing the allocation probability toward a better treatment. The operating characteristics of the two-stage designs are evaluated through extensive simulation studies and an HIV clinical trial is used for illustration. Numerical results show that the proposed method satisfactorily resolves the issues involved in response-adaptive randomization and delayed response.

In phase I clinical trials with cytostatic agents, toxicity endpoints, as well as efficacy effects, should be taken into consideration for identifying the optimal biological dose (OBD). In the second part of the thesis, a two-stage Bayesian mixture modeling approach is developed, which first locates the maximum tolerated dose (MTD) through a mixture of parametric and nonparametric models, and then determines the most efficacious dose using Bayesian adaptive randomization among multiple candidate models. In the first stage searching for the MTD, a beta-binomial model in conjunction with a probit model as a mixture modeling approach is studied, and decisions are made based on the model that better fits the toxicity data. The model fitting adequacy is measured by the deviance information criterion and the posterior model probability. In the second stage searching for the OBD, the assumption that efficacy monotonically increases with the dose is abandoned and, instead, all the possibilities that each dose could have the highest efficacy effect are enumerated so that the dose-efficacy curve can be increasing, decreasing, or umbrella-shape. Simulation studies show the advantages of the proposed mixture modeling approach for pinpointing the MTD and OBD, and demonstrate its satisfactory performance with cytostatic agents.