Objectives To investigate the use of a Bayesian joint modelling approach to predict overall survival (OS) from immature clinical trial data using an intermediate biomarker. To compare the results with a typical parametric approach of extrapolation and observed survival from a later datacut. Methods Data were pooled from three phase I/II open-label trials evaluating larotrectinib in 196 patients with neurotrophic tyrosine receptor kinase fusion-positive (NTRK+) solid tumours followed up until July 2021. Bayesian joint modelling was used to obtain patient-specific predictions of OS using individual-level sum of diameter of target lesions (SLD) profiles up to the time at which the patient died or was censored. Overall and tumour site-specific estimates were produced, assuming a common, exchangeable, or independent association structure across tumour sites. Results The overall risk of mortality was 9% higher per 10mm increase in SLD (HR 1.09, 95% CrI 1.05 to 1.14) for all tumour sites combined. Tumour-specific point estimates of restricted mean , median and landmark survival were more similar across models for larger tumour groups, compared to smaller tumour groups. In general, parameters were estimated with more certainty compared to a standard Weibull model and were aligned with the more recent datacut. Conclusions Joint modelling using intermediate outcomes such as tumour burden can offer an alternative approach to traditional survival modelling and may improve survival predictions from limited follow-up data. This approach allows complex hierarchical data structures, such as patients nested within tumour types, and can also incorporate multiple longitudinal biomarkers in a multivariate modelling framework.

翻译：目的 探讨利用贝叶斯联合建模方法，通过中间生物标志物从不成熟的临床试验数据中预测总生存期（OS）。将结果与典型的外推参数化方法及后续数据截断点的观察生存期进行比较。方法 数据来源于三项评估拉罗替尼在196例神经营养性酪氨酸受体激酶融合阳性（NTRK+）实体瘤患者中的I/II期开放标签试验，随访至2021年7月。采用贝叶斯联合建模，基于患者死亡或删失前的个体水平靶病灶直径总和（SLD）变化曲线，获得患者特异性的OS预测。在假设肿瘤部位间存在共同、可交换或独立关联结构的前提下，生成总体及肿瘤部位特异性估计值。结果 所有肿瘤部位合并分析显示，SLD每增加10mm，总体死亡风险增加9%（HR 1.09，95% CrI 1.05至1.14）。与较小肿瘤组相比，较大肿瘤组的限制性平均生存期、中位生存期及界标生存期的肿瘤特异性点估计值在不同模型间更为接近。总体而言，相较于标准威布尔模型，此方法参数估计确定性更高，且与更近期的数据截断点结果一致。结论 利用肿瘤负荷等中间结局进行联合建模，可为传统生存建模提供一种替代方法，并可能基于有限随访数据改善生存预测。该方法支持复杂的分层数据结构（如患者嵌套于肿瘤类型中），并可在多变量建模框架中纳入多个纵向生物标志物。