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Modeling time-varying effects of multilevel risk factors of hospitalizations in patients on dialysis.

ABSTRACT: For chronic dialysis patients, a unique population requiring continuous medical care, methodologies to monitor patient outcomes, such as hospitalizations, over time, after initiation of dialysis, are of particular interest. Contributing to patient hospitalizations is a number of multilevel covariates such as demographics and comorbidities at the patient level and staffing composition at the dialysis facility level. We propose a varying coefficient model for multilevel risk factors (VCM-MR) to study the time-varying effects of covariates on patient hospitalization risk as a function of time on dialysis. The proposed VCM-MR also includes subject-specific random effects to account for within-subject correlation and dialysis facility-specific fixed effect varying coefficient functions to allow for the modeling of flexible time-varying facility-specific risk trajectories. An approximate EM algorithm and an iterative Newton-Raphson approach are proposed to address the challenge of estimation of high-dimensional parameters (varying coefficient functions) for thousands of dialysis facilities in the United States. The proposed modeling allows for comparisons between time-varying effects of multilevel risk factors as well as testing of facility-specific fixed effects. The method is applied to model hospitalization risk using the rich hierarchical data available on dialysis patients initiating dialysis between January 1, 2006 and December 31, 2008 from the United States Renal Data System, a large national database, where 331?443 hospitalizations over time are nested within patients, and 89?889 patients are nested within 2201 dialysis facilities. Patients are followed-up until December 31, 2013, where the follow-up time is truncated five years after the initiation of dialysis. Finite sample properties are studied through extensive simulations.

SUBMITTER: Li Y

PROVIDER: S-EPMC6296494 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Modeling time-varying effects of multilevel risk factors of hospitalizations in patients on dialysis.

Li Yihao Y Nguyen Danh V DV Chen Yanjun Y Rhee Connie M CM Kalantar-Zadeh Kamyar K Şentürk Damla D

Statistics in medicine 20180903 30

For chronic dialysis patients, a unique population requiring continuous medical care, methodologies to monitor patient outcomes, such as hospitalizations, over time, after initiation of dialysis, are of particular interest. Contributing to patient hospitalizations is a number of multilevel covariates such as demographics and comorbidities at the patient level and staffing composition at the dialysis facility level. We propose a varying coefficient model for multilevel risk factors (VCM-MR) to st ...[more]

PMID: 30252153

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Project description:Risk factors increase the incidence and severity of chronic disease. To examine future trends and develop policies addressing chronic diseases, it is important to capture the relationship between exposure and disease development, which is challenging given limited data.To develop parsimonious risk factor models embeddable in chronic disease models, which are useful when longitudinal data are unavailable.The model structures encode relevant features of risk factors (e.g., time-varying, modifiable) and can be embedded in chronic disease models. Calibration captures time-varying exposures for the risk factor models using available cross-sectional data. We illustrate feasibility with the policy-relevant example of smoking in India.The model is calibrated to the prevalence of male smoking in 12 Indian regions estimated from the 2009-2010 Indian Global Adult Tobacco Survey. Nelder-Mead searches (250,000 starting locations) identify distributions of starting, quitting, and restarting rates that minimize the difference between modeled and observed age-specific prevalence. We compare modeled life expectancies to estimates in the absence of time-varying risk exposures and consider gains from hypothetical smoking cessation programs delivered for 1 to 30 years.Calibration achieves concordance between modeled and observed outcomes. Probabilities of starting to smoke rise and fall with age, while quitting and restarting probabilities fall with age. Accounting for time-varying smoking exposures is important, as not doing so produces smaller estimates of life expectancy losses. Estimated impacts of smoking cessation programs delivered for different periods depend on the fact that people who have been induced to abstain from smoking longer are less likely to restart.The approach described is feasible for important risk factors for numerous chronic diseases. Incorporating exposure-change rates can improve modeled estimates of chronic disease outcomes and of the long-term effects of interventions targeting risk factors.

Dataset Information

Modeling time-varying effects of multilevel risk factors of hospitalizations in patients on dialysis.

Publications

Modeling time-varying effects of multilevel risk factors of hospitalizations in patients on dialysis.

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