发表机构
Global Statistical Sciences, Eli Lilly and Company; Department of Biostatistics, The University of Texas MD Anderson Cancer Center; Population Health Sciences Institute, Newcastle University（礼来公司全球统计科学部; 德克萨斯大学安德森癌症中心生物统计学系; 纽卡斯尔大学人口健康科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对肿瘤联合试验剂量优化，提出PKComb-BOIN12贝叶斯设计，将PK暴露指标融入剂量递增与可接受集约束，通过序理想消除和分层校准提升决策准确性，并扩展至迟发结局。
AI 中文摘要
早期肿瘤学试验中两药联合的剂量优化必须同时平衡毒性、疗效和具有药理学意义的药物暴露，然而现有的模型辅助联合设计仅使用二元毒性-疗效结局，且未将药代动力学（PK）数据用于剂量决策。我们提出PKComb-BOIN12，一种基于效用的贝叶斯最优区间设计，通过PK引导的剂量递增规则和最终可接受集上的PK充分性约束，将连续PK暴露指标嵌入两药剂量优化中。该设计区别于现有单药PK知情设计的直接联合扩展之处在于两项贡献。首先，基于PK的消除采用坐标方向序理想（下集）移除规则，因此，因暴露不足而消除某个联合剂量组合的许可，恰好由暴露在每个药物剂量上的假定单调性所决定，而非基于联合剂量强度的不合理标量汇总。其次，预先指定的目标暴露被视为可转移但可验证：一种分层校准机制利用累积的联合组PK数据更新从单药治疗推导出的假定目标，并在可转移性违反情况下对由此产生的选择偏差给出正式界限。一种时间-事件扩展，TITE-PKComb-BOIN12，可处理迟发结局。一项涵盖现实场景的因子模拟研究（包括一个校准至已发表联合试验的场景）分解了PKComb-BOIN12相对于其非PK前身性能提升的来源，并评估了对非单调暴露、PK方差错误设定和目标可转移性违反的稳健性。一个R Shiny应用程序支持设计评估和实时实施。
英文摘要
Dose optimisation of two-agent combinations in early-phase oncology trials must jointly balance toxicity, efficacy, and pharmacologically meaningful drug exposure, yet existing model-assisted combination designs use only binary toxicity--efficacy outcomes and leave pharmacokinetic (PK) data unused for dosing decisions. We propose PKComb-BOIN12, a utility-based Bayesian optimal interval design embedding a continuous PK exposure metric into two-agent dose optimisation via a PK-guided escalation rule and a PK-sufficiency constraint on the final admissible set. Two contributions distinguish this design from a direct combination extension of existing single-agent PK-informed designs. First, PK-based elimination uses a coordinatewise order-ideal (down-set) removal rule, so that eliminating a combination for insufficient exposure is licensed exactly by the assumed monotonicity of exposure in each agent's dose, rather than an unjustified scalar summary of combined dose intensity. Second, the pre-specified target exposure is treated as transportable-but-verifiable: a hierarchical calibration mechanism updates the assumed monotherapy-derived target using accruing combination-arm PK data, with a formal bound on the resulting selection bias under transportability violations. A time-to-event extension, TITE-PKComb-BOIN12, accommodates late-onset outcomes. A factorial simulation study spanning realistic scenarios, including one calibrated to a published combination trial, decomposes the source of PKComb-BOIN12's gains over its non-PK predecessor and evaluates robustness to non-monotone exposure, PK-variance misspecification, and target-transportability violations. An R Shiny application supports design evaluation and real-time conduct.
Comments42 pages, 2 Figures, 7 Tables
