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Cardiovascular event risk dynamics over time in older patients on dialysis: A generalized multiple‐index varying coefficient model approach

Biometrics, 2014-09, Vol.70 (3), p.751-761 [Peer Reviewed Journal]

Copyright © 2014 International Biometric Society ;2014, The International Biometric Society ;2014, The International Biometric Society. ;ISSN: 0006-341X ;EISSN: 1541-0420 ;DOI: 10.1111/biom.12176 ;PMID: 24766178 ;CODEN: BIOMA5

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Title:
Cardiovascular event risk dynamics over time in older patients on dialysis: A generalized multiple‐index varying coefficient model approach
Author: Estes, Jason P ; Nguyen, Danh V ; Dalrymple, Lorien S ; Mu, Yi ; Şentürk, Damla
Subjects: Age Distribution ; Aged ; Aged, 80 and over ; Biometrics ; biometry ; Cardiovascular disease ; cardiovascular diseases ; Cardiovascular Diseases - mortality ; Cardiovascular outcomes ; Causality ; Cohort Studies ; Comorbidity ; Data Interpretation, Statistical ; death ; Dialysis ; DISCUSSION PAPER ; End stage renal disease ; Generalized linear models ; Hemodialysis ; Hospitalization ; Humans ; Infection ; Infections ; Inference ; kidney diseases ; Kidney Failure, Chronic - mortality ; Kidney Failure, Chronic - prevention & control ; Longitudinal data ; Maximum likelihood estimation ; Older people ; Outcome Assessment (Health Care) - methods ; patients ; Preliminary estimates ; Prevalence ; Probabilities ; Prognosis ; Renal Dialysis - statistics & numerical data ; risk ; Risk Factors ; Simulations ; Survival Analysis ; Survival Rate ; Time-varying effects ; United States - epidemiology ; United States Renal Data System
Is Part Of: Biometrics, 2014-09, Vol.70 (3), p.751-761
Description: Among patients on dialysis, cardiovascular disease and infection are leading causes of hospitalization and death. Although recent studies have found that the risk of cardiovascular events is higher after an infection‐related hospitalization, studies have not fully elucidated how the risk of cardiovascular events changes over time for patients on dialysis. In this work, we characterize the dynamics of cardiovascular event risk trajectories for patients on dialysis while conditioning on survival status via multiple time indices: (1) time since the start of dialysis, (2) time since the pivotal initial infection‐related hospitalization, and (3) the patient's age at the start of dialysis. This is achieved by using a new class of generalized multiple‐index varying coefficient (GM‐IVC) models. The proposed GM‐IVC models utilize a multiplicative structure and one‐dimensional varying coefficient functions along each time and age index to capture the cardiovascular risk dynamics before and after the initial infection‐related hospitalization among the dynamic cohort of survivors. We develop a two‐step estimation procedure for the GM‐IVC models based on local maximum likelihood. We report new insights on the dynamics of cardiovascular events risk using the United States Renal Data System database, which collects data on nearly all patients with end‐stage renal disease in the United States. Finally, simulation studies assess the performance of the proposed estimation procedures.
Publisher: United States: Blackwell Publishers
Language: English;French
Identifier: ISSN: 0006-341X
EISSN: 1541-0420
DOI: 10.1111/biom.12176
PMID: 24766178
CODEN: BIOMA5
Source: MEDLINE
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Cardiovascular event risk dynamics over time in older patients on dialysis: A generalized multiple‐index varying coefficient model approach

Copyright © 2014 International Biometric Society ;2014, The International Biometric Society ;2014, The International Biometric Society. ;ISSN: 0006-341X ;EISSN: 1541-0420 ;DOI: 10.1111/biom.12176 ;PMID: 24766178 ;CODEN: BIOMA5

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