Random Weighted Bootstrap Method For Recurrent Events With Informative Censoring
Date Issued
2005-07-31
Date
2005-07-31
Author(s)
DOI
932118M002001
Abstract
Using the data from the AIDS Link to Intravenous Experiences cohort study as an
example, an informative censoring model was used to characterize the repeated hospitalization
process of a group of patients. Under the informative censoring assumption,
the estimators of the baseline rate function and the regression parameters were shown
to be influenced by a latent variable in the considered model. It becomes impractical
to directly estimate the unknown quantities in the moments of the estimators for the
bandwidth selection of a smoothing estimator and the construction of confidence intervals,
which are respectively based on the asymptotic mean squared errors and the
asymptotic distributions of the estimators. To overcome these difficulties, we develop
a random weighted bootstrap procedure to select appropriate bandwidths and to construct
approximated confidence intervals. One can see that our method is simple and
faster to implement from a practical point of view, and is at least as accurate as other
bootstrap methods. In this article, it is shown that the proposed method is useful
through the performance of a Monte Carlo simulation. An application of our procedure
is also illustrated by a recurrent event sample of intravenous drug users for inpatient
cares over time.
SDGs
Publisher
臺北市：國立臺灣大學數學系暨研究所
Type
report
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