Non-parametric methods for recurrent event data with informative and non-informative censorings
Resource
STATISTICS IN MEDICINE 21,445-456
Journal
Statistics in Medicine
Pages
445-456
Date Issued
2002
Date
2002
Author(s)
DOI
20060927121125210253
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.
Subjects
cumulative rate function
informative censoring
intensity function
kernal estimation
rate function
recurrent events
Other Subjects
acquired immune deficiency syndrome; article; cohort analysis; confidentiality; controlled study; data analysis; human; intravenous drug abuse; longitudinal study; major clinical study; outcomes research; patient information; recurrence risk; risk assessment; simulation; statistical analysis; time; Acquired Immunodeficiency Syndrome; Computer Simulation; Hospitalization; Humans; Longitudinal Studies; Models, Statistical; Monte Carlo Method; Statistics, Nonparametric; Substance Abuse, Intravenous
Type
journal article
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