| Literature DB >> 28716856 |
Wei Yang1,2, Christopher Jepson2, Dawei Xie3,2, Jason A Roy3,2, Haochang Shou3,2, Jesse Yenchih Hsu3,2, Amanda Hyre Anderson3,2, J Richard Landis3,2, Jiang He4, Harold I Feldman3,2.
Abstract
Cardiovascular events, such as hospitalizations because of congestive heart failure, often occur repeatedly in patients with CKD. Many studies focus on analyses of the first occurrence of these events, and discard subsequent information. In this article, we review a number of statistical methods for analyzing ordered recurrent events of the same type, including Poisson regression and three commonly used survival models that are extensions of Cox proportional hazards regression. We illustrate the models by analyzing data from the Chronic Renal Insufficiency Cohort Study to identify risk factors for congestive heart failure hospitalizations in patients with CKD. We show that recurrent event analyses provide additional insights about the data compared with a standard survival analysis of time to the first event.Entities:
Keywords: Chronic; Cohort Studies; Cox proportional hazards model; Humans; Poisson regression; Renal Insufficiency; Survival Analysis; chronic kidney disease; heart failure; hospitalization; recurrent event; risk factors
Mesh：
Year: 2017 PMID： 28716856 PMCID： PMC5718286 DOI： 10.2215/CJN.12841216
Source DB: PubMed Journal: Clin J Am Soc Nephrol ISSN： 1555-9041 Impact factor: 8.237