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http://dx.doi.org/10.7465/jkdi.2014.25.2.327

Statistical analysis of recurrent gap time events with incomplete observation gaps  

Shin, Seul Bi (Health Insurance Policy Research Institute, National Health Insurance Corporation)
Kim, Yang Jin (Department of Statistics, Sookmyung Women's University)
Publication Information
Journal of the Korean Data and Information Science Society / v.25, no.2, 2014 , pp. 327-336 More about this Journal
Abstract
Recurrent event data occurs when a subject experiences same type of event repeatedly and is found in various areas such as the social sciences, Economics, medicine and public health. To analyze recurrent event data either a total time or a gap time is adopted according to research interest. In this paper, we analyze recurrent event data with incomplete observation gap using a gap time scale. That is, some subjects leave temporarily from a study and return after a while. But it is not available when the observation gaps terminate. We adopt an interval censoring mechanism for estimating the termination time. Furthermore, to model the association among gap times of a subject, a frailty effect is incorporated into a model. Programs included in Survival package of R program are implemented to estimate the covariate effect as well as the variance of frailty effect. YTOP (Young Traffic Offenders Program) data is analyzed with both proportional hazard model and a weibull regression model.
Keywords
Frailty effect; incomplete observation; interval censoring; recurrent event data;
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Times Cited By KSCI : 3  (Citation Analysis)
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