In many longitudinal studies, we are interested in both repeated measures of a biomarker and time to an event. When there exist heterogeneous patterns of the longitudinal and survival profiles, we propose a latent class joint model to identify subgroups of subjects and study the association between longitudinal and survival outcomes. The model is estimated by maximizing the full likelihood function. We use B-splines to approximate the baseline hazard function which involves a diverging number of parameters. Asymptotic properties of the estimator for the joint latent class model are investigated. We conduct simulation studies to assess the performance of the developed method. A real data example, Mayo Clinic Primary Biliary Cirrhosis Data, is analyzed using the joint modeling approach.

中文翻译：

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具有纵向和生存数据的半参数联合潜在类模型

在许多纵向研究中，我们对生物标志物的重复测量和事件发生的时间都感兴趣。当存在纵向和生存特征的异质模式时，我们提出了一个潜在类联合模型来识别受试者的亚组并研究纵向和生存结果之间的关联。该模型是通过最大化全似然函数来估计的。我们使用 B 样条来近似基线风险函数，它涉及不同数量的参数。研究了联合潜在类模型估计器的渐近特性。我们进行模拟研究以评估所开发方法的性能。使用联合建模方法分析了一个真实的数据示例 Mayo Clinic 原发性胆汁性肝硬化数据。