More than 720,000 patients with end‐stage renal disease in the United States require life‐sustaining dialysis treatment. In this population of typically older patients with a high morbidity burden, hospitalization is frequent at a rate of about twice per patient‐year. Aside from frequent hospitalizations, which is a major source of death risk, overall mortality in dialysis patients is higher than other comparable populations, including Medicare patients with cancer. Thus, understanding patient‐ and facility‐level risk factors that jointly contribute to longitudinal hospitalizations and mortality is of interest. Towards this objective, we propose a novel methodology to jointly model hospitalization, a binary longitudinal outcome, and survival, based on multilevel data from the United States Renal Data System (USRDS), with repeated observations over time nested in patients and patients nested in dialysis facilities. In our approach, the outcomes are modeled through a common set of multilevel random effects. In order to accommodate the USRDS data structure, we depart from the literature on joint modeling of longitudinal and survival data by including multilevel random effects and multilevel covariates, at both the patient and facility levels. An approximate Expectation‐Maximization algorithm is developed for estimation and inference where fully exponential Laplace approximations are utilized to address computational challenges.

中文翻译：

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透析患者住院和生存的多层次联合建模

在美国，有超过72万名终末期肾病患者需要维持生命的透析治疗。在这些通常具有较高发病率负担的年龄较大的患者人群中，住院率很高，每位患者每年大约要住院两次。除了经常住院治疗（这是造成死亡风险的主要来源）外，透析患者的总体死亡率也高于其他可比人群，包括患有癌症的Medicare患者。因此，了解共同导致纵向住院和死亡的患者和设施水平的风险因素是很重要的。为了实现这一目标，我们基于美国肾脏数据系统（USRDS）的多级数据，提出了一种新颖的方法来对住院，二元纵向结果和生存率进行联合建模，随着时间的推移，患者和嵌套在透析设施中的患者反复观察。在我们的方法中，通过一组通用的多级随机效应对结果进行建模。为了适应USRDS数据结构，我们从纵向和生存数据的联合建模出发，通过在患者和设施级别上包括多级随机效应和多级协变量来进行研究。开发了一种近似期望最大化算法，用于估计和推论，其中使用了全指数拉普拉斯近似来解决计算难题。我们从纵向和生存数据联合建模的文献中脱颖而出，包括在患者和机构层面上的多级随机效应和多级协变量。开发了一种近似期望最大化算法，用于估计和推论，其中使用了全指数拉普拉斯近似来解决计算难题。我们从纵向和生存数据联合建模的文献中脱颖而出，包括在患者和机构层面上的多级随机效应和多级协变量。开发了一种近似期望最大化算法，用于估计和推论，其中使用了全指数拉普拉斯近似来解决计算难题。