In biomedical studies it is common to collect data on multiple biomarkers during study follow-up for dynamic prediction of a time-to-event clinical outcome. The biomarkers are typically intermittently measured, missing at some event times, and may be subject to high biological variations, which cannot be readily used as time-dependent covariates in a standard time-to-event model. Moreover, they can be highly correlated if they are from in the same biological pathway. To address these issues, we propose a flexible joint model framework that models the multiple biomarkers with a shared latent reduced rank longitudinal principal component model and correlates the latent process to the event time by the Cox model for dynamic prediction of the event time. The proposed joint model for highly correlated biomarkers is more flexible than some existing methods since the latent trajectory shared by the multiple biomarkers does not require specification of a priori parametric time trend and is determined by data. We derive an expectation-maximization (EM) algorithm for parameter estimation, study large sample properties of the estimators, and adapt the developed method to make dynamic prediction of the time-to-event outcome. Bootstrap is used for standard error estimation and inference. The proposed method is evaluated using simulations and illustrated on a lung transplant data to predict chronic lung allograft dysfunction (CLAD) using chemokines measured in bronchoalveolar lavage fluid of the patients.

中文翻译：

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用于多个纵向生物标志物和事件发生时间结果的灵活联合模型：使用高度相关的生物标志物进行动态预测的应用

在生物医学研究中，通常会在研究随访期间收集多种生物标志物的数据，以动态预测事件发生时间的临床结果。生物标志物通常是间歇性测量的，在某些事件时间会丢失，并且可能会受到高生物变异的影响，这不能很容易地用作标准事件发生时间模型中的时间依赖性协变量。此外，如果它们来自同一生物途径，则它们可能高度相关。为了解决这些问题，我们提出了一个灵活的联合模型框架，该框架使用共享的潜在降阶纵向主成分模型对多个生物标志物进行建模，并通过 Cox 模型将潜在过程与事件时间相关联以动态预测事件时间。所提出的高度相关生物标志物的联合模型比一些现有方法更灵活，因为多个生物标志物共享的潜在轨迹不需要指定先验参数时间趋势并且由数据确定。我们推导出用于参数估计的期望最大化 (EM) 算法，研究估计器的大样本属性，并采用开发的方法对事件发生时间结果进行动态预测。Bootstrap 用于标准误差估计和推理。所提出的方法使用模拟进行评估，并在肺移植数据上进行说明，以使用在患者的支气管肺泡灌洗液中测量的趋化因子来预测慢性肺同种异体移植物功能障碍 (CLAD)。