Estes Jason P, Nguyen Danh V, Dalrymple Lorien S, Mu Yi, Şentürk Damla
Department of Biostatistics, University of California, Los Angeles, California 90095, U.S.A.
Department of Medicine, University of California, Irvine, California 92868, U.S.A.
Biometrics. 2014 Sep;70(3):754-64. doi: 10.1111/biom.12176. Epub 2014 Apr 25.
Among patients on dialysis, cardiovascular disease and infection are leading causes of hospitalization and death. Although recent studies have found that the risk of cardiovascular events is higher after an infection-related hospitalization, studies have not fully elucidated how the risk of cardiovascular events changes over time for patients on dialysis. In this work, we characterize the dynamics of cardiovascular event risk trajectories for patients on dialysis while conditioning on survival status via multiple time indices: (1) time since the start of dialysis, (2) time since the pivotal initial infection-related hospitalization, and (3) the patient's age at the start of dialysis. This is achieved by using a new class of generalized multiple-index varying coefficient (GM-IVC) models. The proposed GM-IVC models utilize a multiplicative structure and one-dimensional varying coefficient functions along each time and age index to capture the cardiovascular risk dynamics before and after the initial infection-related hospitalization among the dynamic cohort of survivors. We develop a two-step estimation procedure for the GM-IVC models based on local maximum likelihood. We report new insights on the dynamics of cardiovascular events risk using the United States Renal Data System database, which collects data on nearly all patients with end-stage renal disease in the United States. Finally, simulation studies assess the performance of the proposed estimation procedures.
在透析患者中，心血管疾病和感染是住院和死亡的主要原因。尽管最近的研究发现，与感染相关的住院后心血管事件的风险更高，但研究尚未完全阐明透析患者心血管事件风险随时间如何变化。在这项研究中，我们通过多个时间指标对生存状态进行条件设定，来刻画透析患者心血管事件风险轨迹的动态变化：（1）透析开始后的时间，（2）自关键的初始感染相关住院后的时间，以及（3）透析开始时患者的年龄。这是通过使用一类新的广义多指标变系数（GM-IVC）模型来实现的。所提出的GM-IVC模型利用乘法结构和沿每个时间和年龄指标的一维变系数函数，来捕捉动态存活队列中初始感染相关住院前后的心血管风险动态变化。我们基于局部最大似然法为GM-IVC模型开发了一种两步估计程序。我们使用美国肾脏数据系统数据库报告了关于心血管事件风险动态变化的新见解，该数据库收集了美国几乎所有终末期肾病患者的数据。最后，模拟研究评估了所提出估计程序的性能。
