Statistical Methods for Recurrent Event Analysis in Cohort Studies of CKD.
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.
['Yang|Wei|W|', 'Jepson|Christopher|C|', 'Xie|Dawei|D|', 'Roy|Jason A|JA|', 'Shou|Haochang|H|', 'Hsu|Jesse Yenchih|JY|', 'Anderson|Amanda Hyre|AH|', 'Landis|J Richard|JR|', 'He|Jiang|J|', 'Feldman|Harold I|HI|', '|||']
D006333:Heart Failure D006760:Hospitalization D006801:Humans D015233:Models, Statistical D016012:Poisson Distribution D016016:Proportional Hazards Models D012008:Recurrence D051436:Renal Insufficiency, Chronic D012307:Risk Factors
recurrent event Cohort Studies hospitalization heart failure Poisson regression Chronic Renal Insufficiency risk factors Survival Analysis Cox proportional hazards model Humans chronic kidney disease