Registered in England & Wales No. Cited by lists all citing articles based on Crossref citations. Key Words: Please note: We are unable to provide a copy of the article, please see our help page How do I view content?To request a reprint or commercial or derivative permissions for this article, please click on the relevant link below. Unable to display preview. Let the recurrence time be the time between two successive recurrent events.
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Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine. The estimators in this class tend to incorporate substantial negative mass, but corresponding proper estimators can be obtained by defining a restricted estimator that is either equal to the unrestricted estimator, or is as close as possible to the unrestricted estimator without violating non-negativity constraints. For more information please visit our Permissions help page. .
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Recurrence times can be treated as a type of correlated survival data in statistical analysis. The second class of estimators includes an estimator arising from the simple empirical double failure hazard, as well more efficient estimators that redistribute singly censored observations within the strips of a partition of the risk region, following Van der Laan’s (1996) repaired NPMLE, as well as related adaptive estimators. Nonparametric estimators of the bivariate survivor function have the potential to provide a basic tool for the display and comparison of survival curves, analogous to the Kaplan-Meier estimator for univariate failure time data. © 2004 Springer Science+Business Media New YorkDOI: https://doi. The appropriateness of the estimators is confirmed by statistical theory and simulations.
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In general, because of the ordinal nature of recurrence times, statistical methods that are appropriate for standard correlated survival data in marginal models from this source not be applicable to recurrence time data. Download preview PDF. The former class of estimators includes the Dabrowska (1988) and Prentice-Cai (1992) estimators, for which a marginal hazard-double failure hazard representation leads to suggestions for several new estimators. Simulation and analysis from schizophrenia data are presented to illustrate the estimators’ performance. org/10.
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These and selected other estimators are compared in simulation studies, leading to a synthesis of available estimation techniques, and to suggestions for future research. A class of nonparametric estimators is introduced. People also read lists articles that other readers of this article have read. In this paper we consider candidate bivariate survivor function estimators that arise either from representations of the survivor function in terms of the marginal survivor functions and double failure hazard, or in terms of the double failure hazard only for a Learn More Here truncated version of the data. Unable to display preview.
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Download preview PDF. Either your web browser doesn’t support Javascript or it is currently turned off. In this article we consider the problem of how to estimate the marginal survival function in nonparametric models. Articles with the Crossref icon will open in a new tab. Available nonparametric estimators include estimators that plug empirical estimators of single and double failure hazard rates into survivor function representations, and versions of nonparametric maximum likelihood estimators (NPMLE) that address uniqueness problems. 1007/978-1-4419-9076-1_8
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-20862-6
Online ISBN: 978-1-4419-9076-1eBook Packages: Springer Book ArchiveRecurrent event data are frequently encountered in studies with longitudinal designs.
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5 Howick Place | London | SW1P 1WGEurope PMC requires Javascript to function effectively. Permission can also be obtained via Rightslink. 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. .