A simple nonparametric two-sample test for the distribution function of event time with interval censored data
A simple nonparametric two-sample test for the distribution function of event time with interval censored data
For the setting of interval censored data in which the event time is not exactly observed but known to be inside a random interval, a simple nonparametric two-sample test, based on empirical estimates of smooth functionals of the distribution function of event time, is developed to compare the distribution functions of event time for two populations. Monte Carlo simulation studies on Weibull distributions show that this test performs quite well. A real data set from an AIDS clinical trial is used to illustrate the test.
asymptotic normality, distribution function of event time, empirical estimate, interval censoring, Monte Carlo simulation, panel count data, pseudolikelihood estimate
643-652
Zhang, Ying
a1a5b530-992a-41b3-94d8-043590122036
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
Wu, Hulin
6ec35e5a-25ea-4eae-8584-56b01b2c1c75
2003
Zhang, Ying
a1a5b530-992a-41b3-94d8-043590122036
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
Wu, Hulin
6ec35e5a-25ea-4eae-8584-56b01b2c1c75
Zhang, Ying, Liu, Wei and Wu, Hulin
(2003)
A simple nonparametric two-sample test for the distribution function of event time with interval censored data.
Journal of Nonparametric Statistics, 15 (6), .
(doi:10.1080/10485250310001624530).
Abstract
For the setting of interval censored data in which the event time is not exactly observed but known to be inside a random interval, a simple nonparametric two-sample test, based on empirical estimates of smooth functionals of the distribution function of event time, is developed to compare the distribution functions of event time for two populations. Monte Carlo simulation studies on Weibull distributions show that this test performs quite well. A real data set from an AIDS clinical trial is used to illustrate the test.
This record has no associated files available for download.
More information
Published date: 2003
Keywords:
asymptotic normality, distribution function of event time, empirical estimate, interval censoring, Monte Carlo simulation, panel count data, pseudolikelihood estimate
Organisations:
Statistics
Identifiers
Local EPrints ID: 30115
URI: http://eprints.soton.ac.uk/id/eprint/30115
ISSN: 1048-5252
PURE UUID: bad3dcaf-7505-4888-9fbb-b5428202314e
Catalogue record
Date deposited: 12 May 2006
Last modified: 16 Mar 2024 02:42
Export record
Altmetrics
Contributors
Author:
Ying Zhang
Author:
Hulin Wu
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics