Spatiotemporal multilevel joint modeling of longitudinal and survival outcomes in end-stage kidney disease.

IF 1.2 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Lifetime Data Analysis Pub Date : 2024-10-01 Epub Date: 2024-10-04 DOI:10.1007/s10985-024-09635-w
Esra Kürüm, Danh V Nguyen, Qi Qian, Sudipto Banerjee, Connie M Rhee, Damla Şentürk
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Abstract

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.

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对终末期肾病的纵向和生存结果进行时空多层次联合建模。
与没有肾衰竭的医保患者队列相比,接受透析治疗的终末期肾病(ESKD)患者死亡率高,住院负担过重。该人群的一个主要兴趣点是了解多层次风险因素对纵向住院和死亡率相关结果的时间动态影响。为此,我们利用了来自美国肾脏数据系统(USRDS)的多层次数据,这是一个几乎包括所有 ESKD 患者的全国性数据库,在该数据库中,随着时间推移的重复测量/住院被嵌套在患者身上,而患者则被嵌套在美国毗连地区的(医疗服务)区域内。我们开发了一种新的时空多层次联合模型(STM-JM),该模型考虑到了上述数据的层次结构,同时考虑到了两个结果在不同地区的时空变化。所提出的 STM-JM 包括多层次(患者和地区层次)风险因素对住院轨迹和死亡率的时变效应,并通过多变量条件自回归相关结构纳入跨空间区域的空间相关性。该方法通过贝叶斯框架进行高效估计和推断,其中多层次变化系数函数是通过薄板样条来实现的。通过模拟研究评估了所提方法的有限样本性能。将所提出的方法应用于 USRDS 数据,凸显了患者和地区层面的风险因素对住院率和死亡率的显著时变影响,并确定了美国住院率和死亡率风险较高的特定透析时间段和空间位置。
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来源期刊
Lifetime Data Analysis
Lifetime Data Analysis 数学-数学跨学科应用
CiteScore
2.30
自引率
7.70%
发文量
43
审稿时长
3 months
期刊介绍: The objective of Lifetime Data Analysis is to advance and promote statistical science in the various applied fields that deal with lifetime data, including: Actuarial Science – Economics – Engineering Sciences – Environmental Sciences – Management Science – Medicine – Operations Research – Public Health – Social and Behavioral Sciences.
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