Clinical studies with time-to-event outcomes often collect measurements of a large number of time-varying covariates over time (e.g., clinical assessments or neuroimaging biomarkers) to build time-sensitive prognostic model. An emerging challenge is that due to resource-intensive or invasive (e.g., lumbar puncture) data collection process, biomarkers may be measured infrequently and thus not available at every observed event time point. Lever-aging all available, infrequently measured time-varying biomarkers to improve prognostic model of event occurrence is an important and challenging problem. In this paper, we propose a kernel-smoothing based approach to borrow information across subjects to remedy infrequent and unbalanced biomarker measurements under a time-varying hazards model. A penalized pseudo-likelihood function is proposed for estimation, and an efficient augmented penalization minimization algorithm related to the alternating direction method of multipliers (ADMM) is adopted for computation. Under some regularity conditions to carefully control approximation bias and stochastic variability, we show that even in the presence of ultra-high dimensionality, the proposed method selects important biomarkers with high probability. Through extensive simulation studies, we demonstrate superior performance in terms of estimation and selection performance compared to alternative methods. Finally, we apply the proposed method to analyze a recently completed real world study to model time to disease conversion using longitudinal, whole brain structural magnetic resonance imaging (MRI) biomarkers, and show a substantial improvement in performance over current standards including using baseline measures only.

中文翻译：

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纳入不规则测量的高维生物标志物的时变危害模型


具有事件时间结果的临床研究通常会收集大量随时间变化的协变量的测量值（例如，临床评估或神经影像生物标志物），以建立时间敏感的预后模型。一个新出现的挑战是，由于资源密集型或侵入性（例如腰椎穿刺）数据收集过程，生物标志物可能很少被测量，因此无法在每个观察到的事件时间点都可用。利用所有可用的、不经常测量的时变生物标志物来改善事件发生的预后模型是一个重要且具有挑战性的问题。在本文中，我们提出了一种基于核平滑的方法来跨受试者借用信息，以纠正时变危险模型下不频繁且不平衡的生物标志物测量。提出了惩罚伪似然函数进行估计，并采用与乘子交替方向法（ADMM）相关的高效增强惩罚最小化算法进行计算。在一些规律性条件下仔细控制近似偏差和随机变异性，我们表明，即使存在超高维，所提出的方法也能以高概率选择重要的生物标志物。通过广泛的模拟研究，我们展示了与替代方法相比在估计和选择性能方面的优越性能。最后，我们应用所提出的方法来分析最近完成的一项现实世界研究，以使用纵向全脑结构磁共振成像（MRI）生物标记来模拟疾病转化时间，并显示出性能比当前标准（包括仅使用基线测量）有显着改善。