Department of Biostatistics, University of California, Los Angeles, California.
Department of Medicine, UC Irvine School of Medicine, Orange, California.
Stat Med. 2018 Dec 30;37(30):4707-4720. doi: 10.1002/sim.7950. Epub 2018 Sep 3.
For chronic dialysis patients, a unique population requiring continuous medical care, methodologies to monitor patient outcomes, such as hospitalizations, over time, after initiation of dialysis, are of particular interest. Contributing to patient hospitalizations is a number of multilevel covariates such as demographics and comorbidities at the patient level and staffing composition at the dialysis facility level. We propose a varying coefficient model for multilevel risk factors (VCM-MR) to study the time-varying effects of covariates on patient hospitalization risk as a function of time on dialysis. The proposed VCM-MR also includes subject-specific random effects to account for within-subject correlation and dialysis facility-specific fixed effect varying coefficient functions to allow for the modeling of flexible time-varying facility-specific risk trajectories. An approximate EM algorithm and an iterative Newton-Raphson approach are proposed to address the challenge of estimation of high-dimensional parameters (varying coefficient functions) for thousands of dialysis facilities in the United States. The proposed modeling allows for comparisons between time-varying effects of multilevel risk factors as well as testing of facility-specific fixed effects. The method is applied to model hospitalization risk using the rich hierarchical data available on dialysis patients initiating dialysis between January 1, 2006 and December 31, 2008 from the United States Renal Data System, a large national database, where 331 443 hospitalizations over time are nested within patients, and 89 889 patients are nested within 2201 dialysis facilities. Patients are followed-up until December 31, 2013, where the follow-up time is truncated five years after the initiation of dialysis. Finite sample properties are studied through extensive simulations.
对于需要持续医疗护理的慢性透析患者这一独特群体，监测患者在开始透析后随时间推移的住院等结果的方法特别有趣。导致患者住院的因素有许多，包括患者层面的人口统计学和合并症等多层次协变量，以及透析机构层面的人员配备构成。我们提出了一种用于多层次风险因素的变系数模型（VCM-MR），以研究协变量随时间对患者住院风险的时变影响，其函数为透析时间。所提出的 VCM-MR 还包括个体特定的随机效应，以解释个体内相关性，以及透析机构特定的固定效应变系数函数，以允许建模灵活的随时间变化的机构特定风险轨迹。我们提出了一种近似 EM 算法和迭代牛顿-拉普森方法，以解决在美国数千个透析机构的高维参数（变系数函数）估计问题。所提出的模型允许比较多层次风险因素的时变效应，并测试机构特定的固定效应。该方法应用于使用美国肾脏数据系统（一个大型国家数据库）中 2006 年 1 月 1 日至 2008 年 12 月 31 日期间开始透析的透析患者的丰富层次数据建模住院风险，其中 331443 次住院随时间嵌套在患者中，89889 个患者嵌套在 2201 个透析机构中。患者随访至 2013 年 12 月 31 日，随访时间在透析开始后五年截止。通过广泛的模拟研究了有限样本特性。
