alexa Nonparametric Estimation of a Recurrent Survival Function.


Journal of Psychiatry

Author(s): Wang MC, Chang SH, Wang MC, Chang SH

Abstract Share this page

Abstract Recurrent event data are frequently encountered in studies with longitudinal designs. Let the recurrence time be the time between two successive recurrent events. Recurrence times can be treated as a type of correlated survival data in statistical analysis. In general, because of the ordinal nature of recurrence times, statistical methods that are appropriate for standard correlated survival data in marginal models may not be applicable to recurrence time data. Specifically, for estimating the marginal survival function, the Kaplan-Meier estimator derived from the pooled recurrence times serves as a consistent estimator for standard correlated survival data but not for recurrence time data. In this article we consider the problem of how to estimate the marginal survival function in nonparametric models. A class of nonparametric estimators is introduced. The appropriateness of the estimators is confirmed by statistical theory and simulations. Simulation and analysis from schizophrenia data are presented to illustrate the estimators' performance.
This article was published in J Am Stat Assoc and referenced in Journal of Psychiatry

Relevant Expert PPTs

Relevant Speaker PPTs

Recommended Conferences

Relevant Topics

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version