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Non‐parametric methods for recurrent event data with informative and non‐informative censorings
Author(s) -
Wang MeiCheng,
Chiang ChinTsang
Publication year - 2002
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1029
Subject(s) - censoring (clinical trials) , computer science , parametric statistics , event data , nonparametric statistics , event (particle physics) , data set , parametric model , statistics , econometrics , longitudinal data , data mining , artificial intelligence , covariate , machine learning , mathematics , physics , quantum mechanics
Recurrent event data are commonly encountered in health‐related longitudinal studies. In this paper time‐to‐events models for recurrent event data are studied with non‐informative and informative censorings. In statistical literature, the risk set methods have been confirmed to serve as an appropriate and efficient approach for analysing recurrent event data when censoring is non‐informative. This approach produces biased results, however, when censoring is informative for the time‐to‐events outcome data. We compare the risk set methods with alternative non‐parametric approaches which are robust subject to informative censoring. In particular, non‐parametric procedures for the estimation of the cumulative occurrence rate function (CORF) and the occurrence rate function (ORF) are discussed in detail. Simulation and an analysis of data from the AIDS Link to Intravenous Experiences Cohort Study is presented. Copyright © 2002 John Wiley & Sons, Ltd.