A simple nonparametric two-sample test for the distribution function of event time with interval censored data
Authors:
Ying Zhang a;
Wei Liu b;
Hulin Wu c
| Affiliations: | a Department of Statistics and Actuarial Science, University of Central Florida, Orlando, FL, USA |
| b Department of Mathematics, University of Southampton, Southampton, UK | |
| c Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA |
DOI:
10.1080/10485250310001624530
Publication Frequency:
8 issues per year
Published in:
Journal of Nonparametric Statistics,
Volume
15,
Issue
6
December
2003
, pages 643
- 652
Subjects:
Mathematical Economics;
Mathematical Finance;
Medical Statistics;
Statistical Theory & Methods;
Statistics;
Statistics for the Biological Sciences;
Stochastic Models & Processes;
Number of References: 32
Formats available:
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(English)
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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.
|
| Keywords: Asymptotic normality; Distribution function of event time; Empirical estimate; Interval censoring; Monte Carlo Simulation; Panel Count Data; Pseudolikelihood estimate |
| view references (32) |

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