Department of Statistics, University of California, Riverside, CA, 92521, USA.
Department of Medicine, University of California Irvine, Orange, CA, 92868, USA.
Lifetime Data Anal. 2024 Oct;30(4):827-852. doi: 10.1007/s10985-024-09635-w. Epub 2024 Oct 4.
Individuals with end-stage kidney disease (ESKD) on dialysis experience high mortality and excessive burden of hospitalizations over time relative to comparable Medicare patient cohorts without kidney failure. A key interest in this population is to understand the time-dynamic effects of multilevel risk factors that contribute to the correlated outcomes of longitudinal hospitalization and mortality. For this we utilize multilevel data from the United States Renal Data System (USRDS), a national database that includes nearly all patients with ESKD, where repeated measurements/hospitalizations over time are nested in patients and patients are nested within (health service) regions across the contiguous U.S. We develop a novel spatiotemporal multilevel joint model (STM-JM) that accounts for the aforementioned hierarchical structure of the data while considering the spatiotemporal variations in both outcomes across regions. The proposed STM-JM includes time-varying effects of multilevel (patient- and region-level) risk factors on hospitalization trajectories and mortality and incorporates spatial correlations across the spatial regions via a multivariate conditional autoregressive correlation structure. Efficient estimation and inference are performed via a Bayesian framework, where multilevel varying coefficient functions are targeted via thin-plate splines. The finite sample performance of the proposed method is assessed through simulation studies. An application of the proposed method to the USRDS data highlights significant time-varying effects of patient- and region-level risk factors on hospitalization and mortality and identifies specific time periods on dialysis and spatial locations across the U.S. with elevated hospitalization and mortality risks.
患有终末期肾病（ESKD）并接受透析的个体相对于没有肾衰竭的可比医疗保险患者队列，其死亡率和住院负担随着时间的推移而增加。该人群的一个主要关注点是了解导致纵向住院和死亡率相关结果的多层次风险因素的时间动态效应。为此，我们利用来自美国肾脏数据系统（USRDS）的多层次数据，该系统是一个包含几乎所有 ESKD 患者的国家数据库，其中随着时间的推移，重复测量/住院治疗被嵌套在患者中，而患者则嵌套在整个美国的（医疗服务）区域内。我们开发了一种新颖的时空多层次联合模型（STM-JM），该模型考虑了数据的上述层次结构，同时考虑了区域之间的时空变化对两个结果的影响。所提出的 STM-JM 包括多层次（患者和区域水平）风险因素对住院轨迹和死亡率的时变效应，并通过多元条件自回归相关结构纳入了空间区域之间的空间相关性。通过贝叶斯框架进行有效的估计和推断，其中通过薄板样条针对多层次变化系数函数。通过模拟研究评估了所提出方法的有限样本性能。对 USRDS 数据的应用突出了患者和区域水平风险因素对住院和死亡率的时变影响，并确定了透析期间的特定时间段和美国各地的住院和死亡率风险升高的空间位置。
