ESTIMATING MARGINAL EFFECTS IN ACCELERATED FAILURE TIME MODELS FOR SERIAL SOJOURN TIMES AMONG REPEATED EVENTS
Resource
LIFETIME DATA ANALYSIS v.10 n.2 pp.175-190
Journal
LIFETIME DATA ANALYSIS
Journal Volume
v.10
Journal Issue
n.2
Pages
175-190
Date Issued
2004
Date
2004
Author(s)
CHANG, SHU-HUI
Abstract
Recurrent event data are commonly encountered in longitudinal studies when events occur repeatedly over time for each study subject. An accelerated failure time (AFT) model on the sojourn time between recurrent events is considered in this article. This model assumes that the covariate effect and the subject-specific frailty are additive on the logarithm of sojourn time, and the covariate effect maintains the same over distinct episodes, while the distributions of the frailty and the random error in the model are unspecified. With the ordinal nature of recurrent events, two scale transformations of the sojourn times are derived to construct semiparametric methods of log-rank type for estimating the marginal covariate effects in the model. The proposed estimation approaches/inference procedures also can be extended to the bivariate events, which alternate themselves over time. Examples and comparisons are presented to illustrate the performance of the proposed methods.
Subjects
accelerated failure time (AFT) model
recurrent event
log- rank statistic
Type
journal article