NONPARAMETRIC ESTIMATION OF A RECURRENT SURVIVAL FUNCTION
Resource
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION v.94 n.445 pp.146-153
Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Journal Volume
v.94
Journal Issue
n.445
Pages
146-153
Date Issued
1999
Date
1999
Author(s)
Wang, Mei-Cheng
Chang, Shu-Hui
Abstract
Recurrent event data are frequently encountered in studies
with longitudinal designs. Let the recurrence rime 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.
Subjects
correlated survival data
frailty
Kaplan-Meier estimate
longitudinal designs
recurrent event
Type
journal article