This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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336
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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ENAR
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Abstract - #306415 |
Title:
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Nonparametric Tests for Right-Censored Data with Biased Sampling
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Author(s):
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Yu Shen and Jing Ning*+ and Jing Qin
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Companies:
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MD Anderson Cancer Center and The University of Texas Health Science Center at Houston and National Institute of Allergy and Infectious Diseases
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Address:
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Division of Biostatistics, School of Public Health, Houston, TX, 77030, USA
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Keywords:
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Dependent censoring ;
Length-biased sampling ;
Logrank test ;
Prevalent cohort ;
Score test
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Abstract:
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Right-censored time-to-event data are often observed from a cohort of prevalent cases that are subject to length-biased sampling. Although the issues about biased inference caused by length-biased sampling have been widely recognized in statistical, epidemiological and economical literature, there is no satisfactory solution for efficient two-sample testing. We propose an asymptotic most efficient nonparametric test by properly adjusting for length-biased sampling. The test statistic is derived from a full likelihood function, and can be generalized from the two-sample test to a $k$-sample test. The asymptotic properties of the test statistic under the null hypothesis are derived. The methods are confirmed through extensive simulations and illustrated by application to data from a study of a prevalent cohort of { dementia} patients.
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Authors who are presenting talks have a * after their name.
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