A Bayesian dose-finding design for drug combination clinical trials based on the logistic model

作者：Riviere Marie Karelle; Yuan Ying; Dubois Frederic; Zohar Sarah

来源：Pharmaceutical Statistics, 2014, 13(4): 247-257.

DOI：10.1002/pst.1621

• Bayesian inference
• dose finding
• drug combination
• oncology
• phase I trial

In early phase dose-finding cancer studies, the objective is to determine the maximum tolerated dose, defined as the highest dose with an acceptable dose-limiting toxicity rate. Finding this dose for drug-combination trials is complicated because of drug-drug interactions, and many trial designs have been proposed to address this issue. These designs rely on complicated statistical models that typically are not familiar to clinicians, and are rarely used in practice. The aim of this paper is to propose a Bayesian dose-finding design for drug combination trials based on standard logistic regression. Under the proposed design, we continuously update the posterior estimates of the model parameters to make the decisions of dose assignment and early stopping. Simulation studies show that the proposed design is competitive and outperforms some existing designs. We also extend our design to handle delayed toxicities.

• 出版日期2014-8