发表机构
Population Health Sciences Institute, Newcastle University; Quantitative Science and Evidence Generation, Astellas Pharma Global Development Inc.（纽卡斯尔大学人口健康科学研究所; 安斯泰来全球开发公司定量科学与证据生成部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对联合肿瘤学试验中的最优生物学剂量组合发现，提出设计分类路线图及稳健的跨适应症贝叶斯借用方法，并通过模拟和案例验证其有效性。
AI 中文摘要
早期阶段联合肿瘤学试验越来越多地寻求最优生物学剂量组合（OBDC），而非最大耐受剂量组合，这与FDA的Project Optimus倡议一致。现有的贝叶斯OBDC设计涵盖基于规则、模型辅助和基于模型的范式，但缺乏统一框架来指导选择，且均不支持在共享同一联合方案的适应症之间借用信息。我们提出一个修正的OBDC设计三维两分类法（按机制和目标），一个基于规则的设计（Ji3+3-Comb），填补了透明、无模型组合剂量发现中的已知空白，以及一个贝叶斯分层效用跨适应症（BHUC）设计，该设计通过稳健的混合先验在适应症间借用信息，同时折现不可交换的信息。我们将共享效用函数推导为在线性临床损失下的贝叶斯最优决策，并证明BHUC的混合先验后验权重在两种适应症的真实率变得不一致时自动消失，从而形式化其对不可交换借用的稳健性，并以一个精确的、无样本量依赖的借用影响上限补充这一渐近保证，该上限即使在自身适应症数据积累之前也成立。一个路线图将试验特征与设计选择联系起来，一项5000次重复的模拟研究、敏感性分析以及基于已发表的Ib期试验构建的案例研究表明，模型辅助设计提供了最稳健的安全-疗效权衡，而BHUC即使在不一致情况下也优于独立的按适应症设计的选择正确性。最后，我们为制药和试验生物统计学家提供具体的、可操作的方案建议，并指出未来方法论和计算发展的方向。
英文摘要
Early-phase combination oncology trials increasingly seek the optimal biological dose combination (OBDC) rather than a maximum tolerated dose combination, in line with the FDA's Project Optimus initiative. Existing Bayesian OBDC designs span rule-based, model-assisted, and model-based paradigms, but no unified framework exists for choosing among them, and none supports borrowing information across indications sharing a combination regimen. We propose a corrected three-by-two taxonomy of OBDC designs by mechanism and objective, a rule-based design (Ji3+3-Comb) closing a documented gap in transparent, model-free combination dose-finding, and a Bayesian Hierarchical Utility-based Cross-indication (BHUC) design that borrows information across indications via a robust mixture prior while discounting non-exchangeable information. We derive the shared utility function as the Bayes-optimal decision under a linear clinical loss, and prove that BHUC's mixture-prior posterior weight automatically vanishes as two indications' true rates become discordant, formalizing its robustness to non-exchangeable borrowing, and complement this asymptotic guarantee with an exact, sample-size-free ceiling on borrowed influence that holds even before any own-indication data accrue. A roadmap links trial features to design choice, and a 5000-replication simulation study, sensitivity analyses, and a case study built from a published phase Ib trial show that model-assisted designs offer the most robust safety-efficacy trade-off, while BHUC improves correct selection over independent per-indication designs even under discordance. We conclude with concrete, protocol-actionable recommendations for pharmaceutical and trial biostatisticians, and directions for future methodological and computational development.
Comments47 pages, 6 Figures
