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Activity Number:
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561
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #303703 |
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Title:
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Analyzing Bivariate Survival Data with Interval Sampling and Application to Cancer Epidemiology
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Author(s):
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Hong Zhu*+ and Mei-Cheng Wang
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Companies:
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Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health
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Address:
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Department of Biostatistics, Baltimore, MD, 21205,
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Keywords:
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bivariate survival data ; semiparametric ; interval sampling ; cancer epidemiology
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Abstract:
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In the medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. For example, in cancer studies interest could be focused on time from birth to cancer-onset and time from cancer onset to death. We consider a sampling scheme where the first failure event (cancer-onset) is identified within a calendar time interval, the time-origin (birth) can be retrospectively confirmed, and the occurrence of the second event (death) is observed subject to right censoring. It is important to recognize the presence of bias arising due to interval sampling. We developed semiparametric methods for estimating the bivariate survival function under stationary and semi-stationary conditions. Simulation studies is conducted and application to SEER ovarian cancer registries data is presented.
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