Over the last 20 or more years a lot of clinical applications and methodological development in the area of joint models of longitudinal and time-to-event outcomes have come up. In these studies, patients are followed until an event, such as death, occurs. In most of the work, using subject-specific random-effects as frailty, the dependency of these two processes has been established. In this article, we propose a new joint model that consists of a linear mixed-effects model for longitudinal data and an accelerated failure time model for the time-to-event data. These two sub-models are linked via a latent random process. This model will capture the dependency of the time-to-event on the longitudinal measurements more directly. Using standard priors, a Bayesian method has been developed for estimation. All computations are implemented using OpenBUGS. Our proposed method is evaluated by a simulation study, which compares the conditional model with a joint model with local independence by way of calibration. Data on Duchenne muscular dystrophy (DMD) syndrome and a set of data in AIDS patients have been analysed.

在过去的 20 年或更长时间中，在纵向和事件发生时间结果的联合模型领域出现了许多临床应用和方法学开发。在这些研究中，对患者进行跟踪，直到发生诸如死亡之类的事件。在大多数工作中，使用特定于主题的随机效应作为脆弱性，这两个过程的依赖性已经建立。在本文中，我们提出了一种新的联合模型，该模型由纵向数据的线性混合效应模型和事件时间数据的加速失效时间模型组成。这两个子模型通过潜在随机过程联系起来。该模型将更直接地捕获事件时间对纵向测量的依赖性。使用标准先验，已经开发了一种用于估计的贝叶斯方法。所有计算均使用 OpenBUGS 实现。我们提出的方法通过模拟研究进行评估，通过校准将条件模型与具有局部独立性的联合模型进行比较。对杜氏肌营养不良症 (DMD) 综合征的数据和 AIDS 患者的一组数据进行了分析。