Phase I drug-combination trials are becoming commonplace in oncology. Most of the current dose-finding designs aim to quantify the toxicity probability space using certain prespecified yet complicated models. These models need to characterize not only each individual drug’s toxicity profile, but also their interaction effects, which often leads to multi-parameter models. We propose a novel Bayesian adaptive design for drug-combination trials based on a robust dimension-reduction method. We continuously update the order of dose combinations and reduce the two-dimensional searching space to a one-dimensional line based on the estimated order. As a result, the common approaches to single-agent dose finding, such as the continual reassessment method (CRM), can be applied to drug-combination trials. We further utilize the ensemble technique in machine learning, the so-called bootstrap aggregating (bagging) in conjunction with Bayesian model averaging, to enhance the efficiency and reduce the variability of the proposed method. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various scenarios. Compared with existing competitive designs, the bagging CRM demonstrates its precision and robustness in terms of pinning down the correct dose combination. We apply the proposed bagging CRM to two recent cancer clinical trials with combined drugs for dose finding.
"Bootstrap aggregating continual reassessment method for dose finding in drug-combination trials." Ann. Appl. Stat. 10 (4) 2349 - 2376, December 2016. https://doi.org/10.1214/16-AOAS982