CONDITIONAL REGRESSION ANALYSIS FOR RECURRENCE TIME DATA
Resource
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION v.94 n.448 pp.1221-1230
Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Journal Volume
v.94
Journal Issue
n.448
Pages
1221-1230
Date Issued
1999
Date
1999
Author(s)
Chang, Shu-Hui
Abstract
Recurrence time data can be regarded as a specific type of
correlated survival data in which recurrent event times of a
subject are stochastically ordered. Given the ordinal
nature of recurrence times, this article focuses on
conditional regression analysis. A semiparametric hazards
model, including the structural and episode-specific
parameters, is proposed for recurrence time data. In this
model the order of episodes serves as the stratification
variable. Estimation of the structural parameter can be
constructed on the basis of all of the observed recurrence
times. The structural parameter is estimated by the profile-
likelihood approach. Although the structural parameter
estimator is asymptotically normal, the episode-specific
parameters may or may not be estimated consistently due to,
the sparseness of data for specific events. Examples are
presented to illustrate the performance of the estimators of
the structural and episode-specific parameters. An
extension of the univariate recurrent events to the
bivariate events, which occur repeatedly and sequentially,
is discussed with an example.
Subjects
correlated survival data
counting processes
martingale
partial likelihood
profile likelihood
Type
journal article