ABSTRACT Joint models are statistical tools for estimating the association between time-to-event and longitudinal outcomes. One challenge to the application of joint models is its computational complexity. Common estimation methods for joint models include a two-stage method, Bayesian and maximum-likelihood methods. In this work, we consider joint models of a time-to-event outcome and multiple longitudinal processes and develop a maximum-likelihood estimation method using the expectation–maximization algorithm. We assess the performance of the proposed method via simulations and apply the methodology to a data set to determine the association between longitudinal systolic and diastolic blood pressure measures and time to coronary artery disease.

中文翻译：

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多个纵向过程和时间到事件结果的联合模型

摘要 联合模型是估计事件发生时间和纵向结果之间关联的统计工具。联合模型应用的一大挑战是其计算复杂性。联合模型的常见估计方法包括两阶段方法、贝叶斯方法和最大似然方法。在这项工作中，我们考虑了时间到事件结果和多个纵向过程的联合模型，并使用期望最大化算法开发了一种最大似然估计方法。我们通过模拟评估所提出方法的性能，并将该方法应用于数据集，以确定纵向收缩压和舒张压测量值与冠状动脉疾病时间之间的关联。