Li Yihao, Nguyen Danh V, Kürüm Esra, Rhee Connie M, Banerjee Sudipto, Şentürk Damla
Department of Biostatistics, University of California, Los Angeles, CA 90095, USA.
Department of Medicine, University of California Irvine, Orange, CA 92868, USA.
Stat. 2022 Dec;11(1). doi: 10.1002/sta4.438. Epub 2021 Nov 19.
Over 785,000 individuals in the U.S. have end-stage renal disease (ESRD) with about 70% of patients on dialysis, a life-sustaining treatment. Dialysis patients experience frequent hospitalizations. In order to identify risk factors of hospitalizations, we utilize data from the large national database, United States Renal Data System (USRDS). To account for the hierarchical structure of the data, with longitudinal hospitalization rates nested in dialysis facilities and dialysis facilities nested in geographic regions across the U.S., we propose a multilevel varying coefficient spatiotemporal model (M-VCSM) where region- and facility-specific random deviations are modeled through a multilevel Karhunen-Loéve (KL) expansion. The proposed M-VCSM includes time-varying effects of multilevel risk factors at the region- (e.g., urbanicity and area deprivation index) and facility-levels (e.g., patient demographic makeup) and incorporates spatial correlations across regions via a conditional autoregressive (CAR) structure. Efficient estimation and inference is achieved through the fusion of functional principal component analysis (FPCA) and Markov Chain Monte Carlo (MCMC). Applications to the USRDS data highlight significant region- and facility-level risk factors of hospitalizations and characterize time periods and spatial locations with elevated hospitalization risk. Finite sample performance of the proposed methodology is studied through simulations.
美国有超过78.5万人患有终末期肾病（ESRD），约70%的患者接受透析这种维持生命的治疗。透析患者经常住院。为了确定住院的风险因素，我们利用了来自大型国家数据库——美国肾脏数据系统（USRDS）的数据。考虑到数据的层次结构，纵向住院率嵌套在透析机构中，而透析机构又嵌套在美国各地的地理区域中，我们提出了一种多层次变系数时空模型（M-VCSM），其中特定区域和机构的随机偏差通过多层次卡尔胡宁-勒夫（KL）展开进行建模。所提出的M-VCSM包括区域层面（如城市化程度和地区贫困指数）和机构层面（如患者人口构成）的多层次风险因素的时变效应，并通过条件自回归（CAR）结构纳入各区域之间的空间相关性。通过功能主成分分析（FPCA）和马尔可夫链蒙特卡罗（MCMC）的融合实现了有效的估计和推断。对USRDS数据的应用突出了住院的显著区域和机构层面风险因素，并刻画了住院风险较高的时间段和空间位置。通过模拟研究了所提出方法的有限样本性能。
