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

Esra Kürüm (), Danh V. Nguyen (), Qi Qian (), Sudipto Banerjee (), Connie M. Rhee () and Damla Şentürk ()
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Esra Kürüm: University of California
Danh V. Nguyen: University of California Irvine
Qi Qian: University of California
Sudipto Banerjee: University of California
Connie M. Rhee: University of California
Damla Şentürk: University of California

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2024, vol. 30, issue 4, No 6, 827-852

Abstract: 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.

Keywords: Conditional autoregressive model; End-stage kidney disease; Markov chain Monte Carlo; United States renal data system; Varying-coefficient models; 62Hxx; 62Nxx; 62P10 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10985-024-09635-w

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