End stage renal disease is associated with accelerated mortality, and cardiovascular (CV) disease is the leading cause of death. Of relevance to more effective care of patients on dialysis is characterizing how outcome trajectories evolve over time and identifying their associated risk factors. Elucidating the time-varying effects of patient-level risk factors, such as infection, and facility-level characteristic, such as facilities' patient care staffing composition, on patients' CV outcome over time, from the start of dialysis is important. Our long-term goal is to provide guidance in identifying modifiable patient-level and facility-level risk factors and approaches to quality improvement of dialysis care providers. Towards this goal, we will develop a general framework to estimation and inference for multilevel time-dynamic modeling of patient outcomes that accommodates multilevel data structures (e.g., patients nested within dialysis facilities or care providers and observations over time nested within patients). Our proposed novel modeling of time- dynamic effects of risk factors of CV events and infection in patients on dialysis, is important for designing effective approaches to disease management and prevention because it allows identification of specific time periods of increased risk. In addition, the proposed framework is o relevant to facility-level decision making, including prediction of whether changes in a dialysis facility's patient care strategy would lead to improved patient outcome over time, as well as time-dynamic performance evaluation. Innovation. To date, there does not exist a feasible framework for estimation and dual inference (patient- and facility-level) in multilevel varying coefficient modeling (MVCM) that accommodates multilevel longitudinal data structures. Our work will be the first to study both patient- and facility-level inference in MVCM simultaneously and to flexibly model facility-level effects that span a spectrum of models, including facility (i) fixed effects, (ii) constant random effects, and (iii) random effects functions of time (random coefficient functions). This will also be the first study to examine continuous dialysis facility performance assessment from initiation of dialysis and allow for identification of specific time periods for targeted patient outcome improvement.
Aims. The proposed framework will be achieved through the following specific aims: 1) (Subject-level Inference) To develop and apply MVCM for multilevel longitudinal response (outcome) with general subject- level covariates and flexible modeling of facility-level effects; 2) (Facility-level Inference) To develop and apply MVCM for facility-level inference; 3) (MVCM Performance Characteristics) To characterize the operational characteristics of MVCM, including relative efficiency and sensitivity to modeling assumptions, across information sparsity levels and inferential goals.
Understanding time-dynamic effects of risk factors on outcomes of patients with chronic diseases, such as cardiovascular events and infections in patients on dialysis, is important for effective disease management and prevention. The application involves developing new methods for multilevel time- dynamic modeling of patient outcomes using the United States Renal Data System. The goal is to provide guidance in identifying modifiable patient-level and facility-level risk factors and approaches to quality improvement of dialysis care providers.
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