Joint models for longitudinal and survival data have become a popular framework for studying the association between repeatedly measured biomarkers and clinical events. Nevertheless, addressing complex survival data structures, especially handling both recurrent and competing event times within a single model, remains a challenge. This causes important information to be disregarded. Moreover, existing frameworks rely on a Gaussian distribution for continuous markers, which may be unsuitable for bounded biomarkers, resulting in biased estimates of associations. To address these limitations, we propose a Bayesian shared-parameter joint model that simultaneously accommodates multiple (possibly bounded) longitudinal markers, a recurrent event process, and competing risks. We use the beta distribution to model responses bounded within any interval (a,b) without sacrificing the interpretability of the association. The model offers various forms of association, discontinuous risk intervals, and both gap and calendar timescales. A simulation study shows that it outperforms simpler joint models. We utilize the US Cystic Fibrosis Foundation Patient Registry to study the associations between changes in lung function and body mass index, and the risk of recurrent pulmonary exacerbations, while accounting for the competing risks of death and lung transplantation. Our efficient implementation allows fast fitting of the model despite its complexity and the large sample size from this patient registry. Our comprehensive approach provides new insights into cystic fibrosis disease progression by quantifying the relationship between the most important clinical markers and events more precisely than has been possible before. The model implementation is available in the R package JMbayes2.

翻译：纵向与生存数据的联合模型已成为研究重复测量生物标志物与临床事件关联的常用框架。然而，处理复杂的生存数据结构，特别是在单一模型中同时处理复发事件时间和竞争事件时间，仍然是一个挑战。这导致重要信息被忽略。此外，现有框架假设连续标记服从高斯分布，这可能不适用于有界生物标志物，从而导致关联估计产生偏差。为应对这些局限，我们提出了一种贝叶斯共享参数联合模型，该模型可同时容纳多个（可能为有界的）纵向标记、一个复发事件过程以及竞争风险。我们使用beta分布对任意区间(a,b)内的有界响应进行建模，且不牺牲关联的可解释性。该模型提供多种关联形式、不连续的风险区间，以及间隔时间尺度和日历时间尺度。模拟研究表明，其性能优于更简单的联合模型。我们利用美国囊性纤维化基金会患者注册数据，研究了肺功能与体重指数变化与复发性肺部急性加重风险之间的关联，同时考虑了死亡和肺移植的竞争风险。尽管模型复杂且患者注册数据样本量大，我们高效的实现仍能快速拟合模型。这一综合方法通过更精确地量化最重要的临床标记与事件之间的关系，为囊性纤维化疾病进展提供了新的见解。模型实现可在R包JMbayes2中获取。