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Activity Number:
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64
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Type:
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Contributed
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Date/Time:
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Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #303650 |
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Title:
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Jointly Modeling Multiple Longitudinal Measurements and Time-to-Event Data
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Author(s):
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Paul S. Albert*+ and Joanna H. Shih
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Companies:
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National Cancer Institute and National Cancer Institute
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Address:
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6130 Executive Blvd. Room 8136, Bethesda, MD, 20906,
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Keywords:
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Longitudinal data analysis ; Joint modeling ; random effects ; Survival data
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Abstract:
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In many medical studies, patients are followed longitudinally and interest is on accessing the relationship between longitudinal measurements and time to an event. Recently, various authors have proposed joint modeling approaches for longitudinal and time-to-event data for a single longitudinal variable. These joint modeling approaches become intractable with even a few longitudinal variables. In this talk, we propose a regression-calibration approach for jointly modeling multiple longitudinal measurements and time-to-event data which appropriately accounts for the informative dropout in the multivariate longitudinal process. With Simulations and data analysis, we show that this approach performs well in estimating the relationship between longitudinal measurements and the time-to-event data and in estimating the parameters of the multiple longitudinal process.
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