This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 336
Type: Topic Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract - #306415
Title: Nonparametric Tests for Right-Censored Data with Biased Sampling
Author(s): Yu Shen and Jing Ning*+ and Jing Qin
Companies: MD Anderson Cancer Center and The University of Texas Health Science Center at Houston and National Institute of Allergy and Infectious Diseases
Address: Division of Biostatistics, School of Public Health, Houston, TX, 77030, USA
Keywords: Dependent censoring ; Length-biased sampling ; Logrank test ; Prevalent cohort ; Score test
Abstract:

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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