由于样本数量有限和剂量探索空间较大，在癌症患者联合治疗的早期开发中获得理想的剂量组合是一项具有挑战性的任务。现有大多数用于优化剂量组合的设计都是基于模型的，需要大量工作来获取参数或先验分布。基于模型的设计还依赖于密集的模型校准，在模型错误规范或数据稀疏的情况下可能导致性能不稳定。我们提出使用局部、参数不足的模型进行剂量探索，以减少模型校准的障碍并增强设计的稳健性。在部分有序连续评估方法的框架下，我们开发了基于局部数据的连续评估方法设计，用于分别识别仅使用毒性评价的最大耐受剂量组合和同时使用毒性和疗效评价的最佳生物剂量组合。基于局部数据的连续评估方法设计仅模拟邻近剂量组合的局部数据。因此，它们在估计局部空间和避免对整个剂量探索表面进行不稳定的描述方面具有灵活性。我们的模拟研究表明，与广泛使用的寻找最大耐受剂量组合的方法相比，我们的方法具有竞争性能，并且相比现有的基于模型的方法优化最佳生物剂量组合具有优势。

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. We propose to employ local, underparameterized 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, we develop local data-based continual reassessment method designs for identifying the maximum tolerated dose combination, using toxicity only, and the optimal biological dose combination, using both toxicity and efficacy, respectively. The local data-based continual reassessment method designs only model the local data from neighboring dose combinations. Therefore, they are flexible in estimating the local space and circumventing unstable characterization of the entire dose-exploration surface. Our simulation studies show that our approach has competitive performance compared to widely used methods for finding maximum tolerated dose combination, and it has advantages over existing model-based methods for optimizing optimal biological dose combination.