AI 中文总结
针对肿瘤学临床试验生存数据不成熟的问题，提出PIONEER贝叶斯联合建模框架，结合肿瘤大小动态与临床事件子模型，通过单次前向模拟得出临床终点，经案例验证可提前预测且量化不确定性，为临床开发决策提供支持。
AI 中文摘要
肿瘤学临床试验中的高风险决策通常要在生存数据不成熟时做出：无进展生存期（PFS）和总生存期（OS）被大量删失，积累的事件很少，主要终点可能要数月或数年才能读出。中期数据截止时可获得的是信息丰富的纵向肿瘤测量值和基线协变量。我们提出了PIONEER，这是一个贝叶斯联合建模框架，它将纵向肿瘤大小动态的机械双组分状态空间子模型与用于竞争临床事件的多状态比例风险子模型相结合，在单个后验下同时拟合。机械子模型从稀疏、有噪声的最长直径总和（SLD）观测值中推断出潜在的个体肿瘤轨迹——分解为具有Gompertz衰减生长的治疗反应性和难治性区室。这些潜在轨迹作为随时间变化的协变量输入多状态风险模型，而事件数据通过联合似然性同时细化肿瘤动态。所有临床终点（PFS、OS、客观缓解率）都在单次前向模拟中从联合后验中得出，传播完整的参数不确定性，无需任何两阶段插入。应用于广泛期小细胞肺癌的案例研究（两项试验，N = 497），留一未来交叉验证表明，在入组第4个月（9名患者）时，该模型产生的校准PFS预测覆盖了成熟的第19个月的Kaplan-Meier曲线，在第11个月（39名患者）时，OS预测收敛——代表至少提前8个月进行预测，且不确定性得到适当量化。我们希望这项工作为贝叶斯机械状态空间框架在临床开发中的更广泛应用铺平道路，使从不成熟试验数据中做出更早、更明智的决策成为可能。
英文摘要
High-stakes decisions in oncology clinical trials must often be made while survival data remains immature: progression-free survival (PFS) and overall survival (OS) are heavily censored, few events have accumulated, and the primary endpoint may be months or years from reading out. What is available at interim data cut-offs is information-rich longitudinal tumour measurements and baseline covariates. We present PIONEER, a Bayesian joint modelling framework that couples a mechanistic two-component state-space submodel of longitudinal tumour size dynamics to a multistate proportional-hazard submodel for competing clinical events, fitted simultaneously under a single posterior. The mechanistic submodel infers latent per-patient tumour trajectories - decomposed into treatment-responsive and refractory compartments with Gompertz-attenuated growth - from sparse, noisy sum-of-longest-diameter (SLD) observations. These latent trajectories feed the multistate hazard as time-varying covariates, while the event data simultaneously refines the tumour dynamics through the joint likelihood. All clinical endpoints (PFS, OS, objective response rate) are derived from the joint posterior in a single forward simulation pass, propagating full parameter uncertainty without any two-stage plug-in. Applied to a case study in extensive-stage small-cell lung cancer (two trials, N = 497), leave-future-out cross-validation demonstrates that at month 4 of enrolment (9 patients) the model produces calibrated PFS forecasts covering the mature month-19 Kaplan-Meier curve, and at month 11 (39 patients) the OS forecast converges - representing at least 8 months of advance forecasting with properly quantified uncertainty. We hope this work paves the way for broader adoption of Bayesian mechanistic state-space frameworks in clinical development, enabling earlier and more informed decision-making from immature trial data.
Comments17 figures, decision making framework, Bayesian statistics, phase II go no-go
