CUI-MET：针对多剂量、多结局随机试验设计的临床效用指数剂量优化方法
CUI-MET: Clinical Utility Index Dose Optimization Approach for Multiple-Dose, Multiple-Outcome Randomized Trial Designs
AI总结：
CUI-MET通过加权多个二分类终点构建临床效用指数，在多剂量随机试验中选出最优剂量，并提供R Shiny工具和Bootstrap稳健性评估，为早期肿瘤试验剂量优化提供灵活易用的方法。
摘要：
肿瘤学临床试验中的剂量优化已从寻求最大耐受剂量转向识别最佳生物学剂量（OBD），即在多个临床属性之间平衡治疗获益与风险。现有的先进剂量探索方法能够整合多个终点并比较剂量水平，但往往较为复杂或计算密集，限制了它们在早期试验中的应用。为应对这些挑战，我们提出了面向多剂量、多结局随机试验设计的临床效用指数剂量优化方法（CUI-MET）。该框架采用基于临床效用的方法整合多个二分类终点，通过对终点响应进行加权，为每个剂量水平计算综合临床效用指数（CUI）。经验方法和建模方法均可估计每个终点的边际概率。随后，利用终点特定权重将这些估计概率组合起来，计算每个剂量的效用得分，并选择得分最高的剂量作为最优剂量。为提升易用性，我们在一个交互式R Shiny应用中实现了这些方法，并通过案例示例展示了其功能。该框架的灵活性允许选择不同的模型和终点加权方案，以反映具体的临床优先级。Bootstrap分析为CUI提供置信区间，并估计每个剂量被选为最优剂量的概率，从而评估剂量选择的稳健性。通过将多个终点整合为单一效用指数并结合用户友好的可视化，CUI-MET为早期肿瘤学试验中的剂量优化提供了一种灵活且易于使用的解决方案，支持知情决策并推进以患者为中心的医疗。
英文摘要：
Dose optimization in oncology clinical trials has shifted from seeking the maximum tolerated dose to identifying the Optimal Biological Dose (OBD) that balances therapeutic benefits and risks across multiple clinical attributes. Existing advanced dose-finding methods can integrate multiple endpoints and compare dose levels but are often complex or computationally intensive, limiting their use in early-phase trials. To address these challenges, we propose the Clinical Utility Index Dose Optimization Approach for Multiple-dose Multiple-Outcome Randomized Trial Designs (CUI-MET). This framework integrates multiple binary endpoints using a clinical utility-based approach, calculating a combined clinical utility index (CUI) for each dose level by weighting endpoint responses. Both empirical and modeling methods can estimate marginal probabilities for each endpoint. These estimated probabilities are then combined using endpoint-specific weights to compute a utility score for each dose, and the dose with the highest score is selected as optimal. To enhance usability, we implemented these methods in an interactive R Shiny application and demonstrated their functionality through case examples. The framework's flexibility allows for different model selections and endpoint weighting schemes to reflect specific clinical priorities. Bootstrap analysis provides confidence intervals for the CUI and estimates the probability that each dose is selected as optimal, thereby evaluating the robustness of dose selection. By integrating multiple endpoints into a single utility index and incorporating user-friendly visualizations, CUI-MET offers a flexible and accessible solution for dose optimization in early-phase oncology trials, supporting informed decision-making and advancing patient-centered care.
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