Great Wall: A Generalized Dose Optimization Design for Drug Combination Trials Maximizing Survival Benefit.

Abstract

Most phase I-II drug-combination trial designs assume that selecting the optimal dose combination based on early outcomes will also lead to maximum long-term survival benefits. However, this assumption is often violated in many clinical studies, generally due to high rates of relapse following the initial response. To address this problem, we propose the Great Wall design, a general dose optimization design for drug-combination trials. The Great Wall design employs a "divide-and-conquer" algorithm to address the issue of partial order of toxicity and uses early outcomes to eliminate dose combinations that are excessively toxic or less efficacious. It utilizes a dose randomization approach to construct a candidate set of the promising dose combinations balancing the toxicity and early efficacy outcomes. The patients assigned to the candidate set are followed to collect the survival outcomes and the final optimal dose combination is then selected to maximize the survival benefit. The simulation studies confirm the desirable operating characteristics of the Great Wall design under various clinical settings. R codes are also provided to facilitate the application. The Great Wall design is modular and practically useful in settings where investigators plan to follow patients long enough to assess survival outcomes.