|Title:||Non-parametric methods for recurrent event data with informative and non-informative censorings||Authors:||CHIN-TSANG CHIANG
|Keywords:||cumulative rate function;informative censoring;intensity function;kernal estimation;rate function;recurrent events||Issue Date:||2002||Start page/Pages:||445-456||Source:||Statistics in Medicine||Abstract:||
Recurrent event data are commonly encountered in health-related longitudinal studies. In this paper timeto-
events models for recurrent event data are studied with non-informative and informative censorings.
In statistical literature, the risk set methods have been con5rmed to serve as an appropriate and e6cient
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.
|Appears in Collections:||數學系|
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