A0431
Title: Local continual reassessment methods for dose finding and optimization in drug-combination trials
Authors: Ruitao Lin - The University of Texas MD Anderson Cancer Center (United States) [presenting]
Abstract: Due to the limited sample size and large dose exploration space, obtaining a desirable dose combination is a challenging task in the early development of combination treatments for cancer patients. Most existing designs for optimizing the dose combination are model-based, requiring significant efforts to elicit parameters or prior distributions. Model-based designs also rely on intensive model calibration and may yield unstable performance in the case of model misspecification or sparse data. The aim is to propose employing local, under-parameterized models for dose exploration to reduce the hurdle of model calibration and enhance the design robustness. Building upon the framework of the partial ordering continual reassessment method (POCRM), local data-based CRM (LOCRM) designs are developed for identifying the maximum tolerated dose combination (MTDC), using toxicity only, and the optimal biological dose combination (OBDC), using both toxicity and efficacy, respectively. The LOCRM designs only model the local data from neighbouring dose combinations. Therefore, they are flexible in estimating the local space and circumventing unstable characterization of the entire dose-exploration surface. The simulation studies show that this approach has competitive performance compared to widely used methods for finding MTDC, and it has advantages over existing model-based methods for optimizing OBDC.
