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Abstract Details

Activity Number: 65
Type: Topic Contributed
Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract - #304022
Title: Empirical Likelihood-Based Confidence Intervals for Length-Biased Data
Author(s): Jing Ning*+ and Jing Qin and Masoud Asgharian and Yu Shen
Companies: MD Anderson Cancer Center and National Institute of Allergy and Infectious Diseases and McGill University and MD Anderson Cancer Center
Address: 6843 Fitzgerald Court, Sugar Land, TX, 77479-4415, United States
Keywords: confidence interval ; length-biased data ; empirical likelihood ratio test ; mean ; median ; survival function

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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