Semiparametric Estimation with Recurrent Event Data under Informative Monitoring
We consider a biomedical study which monitors the occurrences of a recurrent event for n subjects over a random observation window for each subject. We assume that the distribution of the random observation window is informative regarding the distribution of event time. the problem of semiparametric estimation of the cumulative hazard and consequently of the gap-time is considered under a model of informative censoring called the Koziol-Green model. We derive a Nelson-Aalen and Kaplan-Meier type estimators respectively for the cumulative hazard and the gap-time distribution function under the specified model. Asymptotic and small sample properties of the proposed estimators are established. the proposed estimators are compared to the nonparametric estimator of the proposed in Pena et al. (2001, JASA) to ascertain any efficiency gain achieved by exploiting the Koziol-Green structure.
A. Adekpedjou and E. A. Pena, "Semiparametric Estimation with Recurrent Event Data under Informative Monitoring," Proceedings of the 2007 Joint Statistical Meetings (2007, Salt Lake City, UT), American Statistical Association, Jan 2008.
The definitive version is available at https://doi.org/10.1080/10485252.2012.698281
2007 Joint Statistical Meetings (JSM) (2007, Salt Lake City, UT)
Mathematics and Statistics
Article - Conference proceedings
© 2008 American Statistical Association, All rights reserved.
01 Jan 2008