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.
翻译:在生物医学研究中,通常会收集多个生物标志的数据,在研究后续行动中收集多个生物标志的数据,以动态预测时间到事件临床结果。生物标志通常是间歇性测量,在某个事件时缺失,并可能发生高度的生物变化,无法轻易地用作标准时间到事件模型中基于时间的共变数。此外,如果它们来自同一生物路径,它们可能高度相关。为了解决这些问题,我们提议了一个灵活的联合模型框架,用以模拟多个生物标志,以共同的潜在降级纵向主要成分模型为模型,并将潜在过程与Cox模型的事件时间联系起来,用于动态预测事件时间。为高度关联的生物标志提议的联合模型比某些现有方法灵活,因为多个生物标志所共享的潜伏轨迹不需要对前视时间到的时间趋势进行规格说明,而是由数据来确定。我们得出参数估计的预期-最大程度的Broximation(EM)算法,研究测算器的大型样本特性,并调整发达方法,以便利用时间到时间到时间的温度模型,使用预测结果的模型评估。