# Multilevel Time-Dynamic Modeling of Hospitalization and Survival in Patients on Dialysis

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $610,379

## Abstract

PROJECT SUMMARY
Significance. Over 726,000 individuals in the U.S. have end-stage renal disease (ESRD) with about 87% of
patients on dialysis, a life-sustaining treatment. Dialysis patients experience accelerated mortality and frequent
hospitalizations (twice per year). These adverse outcomes are exacerbated at key time periods after transition
to dialysis and show significant spatial variation across U.S. Our overarching goal is to identify modifiable
patient, dialysis facility and region-level (spatial) risk factors and critical time periods for elevated
hospitalization risk and mortality to guide patient care strategies. To reach this goal, we propose novel
multilevel time-dynamic models applied to data from the national database, United States Renal Data System
(USRDS), which contains data on nearly all patients on dialysis in the U.S. We consider three-level longitudinal
data with longitudinal outcomes nested in patients, patients nested in dialysis facilities and facilities nested in
geographic regions across U.S. The proposals include linking of multiple data domains to study effects from
time periods prior to (prelude) and after (vintage) transition to dialysis and accommodate time varying effects of
multilevel risk factors at the patient-, facility- and region-levels.
Aims and Innovation. We propose three specific aims: Aim 1) To develop and apply multilevel spatiotemporal
functional models (MST-FMs) of hospitalization and mortality rates. Initial efforts for identifying spatial “hot
spots” with higher hospitalization and mortality rates have been largely descriptive. In addition, these
approaches do not consider the critical temporal variation across time after transition to dialysis. We will
develop estimation and inference procedures to model the spatially nested functional data of facility-level
hospitalization and mortality rates for constructing spatiotemporal maps, identifying hot spot regions and post
dialysis transition time periods of elevated hospitalization and mortality risk in the dialysis population for the
first time. Aim 2) To develop and apply multilevel time-varying joint models (MT-JMs) of longitudinal
hospitalizations and survival outcomes at the patient-level. The proposed MT-JMs will incorporate functional
predictors from the prelude period, time-varying effects of both cross-sectional and time-dependent multilevel
covariates as well as multilevel random effects. To date, joint modeling approaches mostly consider one-level
hierarchies encompassing longitudinal outcome observed for a set of subjects, with only a few works
considering a two-level hierarchy, intended for modeling time-static (fixed) covariate effects. The proposed MT-
JMs address the need for flexible modeling features (to assess prelude data, monthly clinical longitudinal data
post-transition, multilevel time-varying effects etc.), incorporation of three-level multilevel data structure, and
scalability to complex large datasets. Aim 3) To characteriz...

## Key facts

- **NIH application ID:** 10204626
- **Project number:** 2R01DK092232-08A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Esra Kurum
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $610,379
- **Award type:** 2
- **Project period:** 2011-09-15 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10204626

## Citation

> US National Institutes of Health, RePORTER application 10204626, Multilevel Time-Dynamic Modeling of Hospitalization and Survival in Patients on Dialysis (2R01DK092232-08A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10204626. Licensed CC0.

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