Longitudinal clinical studies often collect repeated measurements of biomarkers or health-related quality of life together with a time-to-event outcome. These processes are intrinsically linked: longitudinal trajectories may predict event risk, while event occurrence, or its anticipation, can induce informative censoring of the longitudinal process. Joint models provide a principled framework for handling this dependence, but most existing formulations rely on proportional hazards assumptions that may be restrictive and offer limited interpretability on the time scale. We propose a class of semiparametric accelerated failure time joint models that directly model covariate effects on event timing while flexibly capturing longitudinal-event associations. The survival component is specified through an accelerated failure time model with the baseline component represented by a flexible basis expansion, allowing a broad class of smooth baseline specifications. We illustrate the framework using Bernstein polynomial baseline representations and introduce rescaling strategies to improve numerical stability and parameter identifiability under time-warping. Estimation is conducted within a Bayesian framework, enabling joint inference for longitudinal, survival, and association parameters. Simulation studies reflecting realistic longitudinal trajectories, censoring mechanisms, and dependence structures are used to evaluate finite-sample performance. The proposed models show improved recovery of longitudinal treatment effects compared with a standalone linear mixed model when event risk depends on the underlying longitudinal process. Overall, the framework extends existing joint modelling methodology by offering a flexible and interpretable alternative to proportional hazards-based approaches.

翻译：纵向临床研究通常同时收集生物标志物或健康相关生活质量的重复测量数据及生存结局时间。这两个过程存在内在关联：纵向轨迹可能预测事件风险，而事件的发生或预期会导致纵向过程出现信息性删失。联合模型为处理这种依赖性提供了统一框架，但现有模型大多依赖比例风险假设，该假设可能具有局限性且时间尺度上的可解释性有限。本文提出一类半参数加速失效时间联合模型，可直接建模协变量对事件时间的影响，同时灵活捕捉纵向过程与事件间的关联。生存分量通过加速失效时间模型表示，其中基线分量由灵活的基函数展开表征，支持多种平滑基线规格。我们使用伯恩斯坦多项式基线表示阐述该框架，并引入重缩放策略以改进时间弯曲条件下的数值稳定性与参数可辨识性。模型在贝叶斯框架下进行估计，实现纵向、生存与关联参数的联合推断。基于反映真实纵向轨迹、删失机制与依赖结构的模拟研究，评估了有限样本性能。当事件风险取决于潜在纵向过程时，与独立线性混合模型相比，所提模型在纵向处理效应恢复方面表现更优。总体而言，该框架通过提供比例风险方法之外兼具灵活性与可解释性的替代方案，拓展了现有联合建模方法论。