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Abstract Details
Activity Number:
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65
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #304022 |
Title:
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Empirical Likelihood-Based Confidence Intervals for Length-Biased Data
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Author(s):
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Jing Ning*+ and Jing Qin and Masoud Asgharian and Yu Shen
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Companies:
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MD Anderson Cancer Center and National Institute of Allergy and Infectious Diseases and McGill University and MD Anderson Cancer Center
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Address:
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6843 Fitzgerald Court, Sugar Land, TX, 77479-4415, United States
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Keywords:
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confidence interval ;
length-biased data ;
empirical likelihood ratio test ;
mean ;
median ;
survival function
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Abstract:
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Logistic or other constraints often preclude the possibility of conducting incident cohort studies. A feasible alternative in such cases is to implement a cross-sectional prevalent cohort study for which we recruit prevalent cases. When the interest lies in estimating the lifespan between the initiating event and a terminating event, say death for instance, such subjects may be followed prospectively until the terminating event or loss to follow-up, whichever happens first. It is well known that prevalent cases have, on average, longer lifespans. If the initiating events are generated from a stationary Poisson process, this bias is called length bias. The current literature on length-biased sampling lacks a simple method for estimating the margin of errors of commonly used summary statistics. We fill this gap using the empirical likelihood-based confidence intervals by adapting this method to right-censored length-biased survival data. Both large and small sample behaviors of these confidence intervals are studied. We illustrate our method using a set of data on survival with dementia, collected as part of the Canadian Study of Health and Aging.
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