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Activity Number:
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13
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 6, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #307070 |
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Title:
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Analysis of Longitudinal Clinical Trial Data with Informative Dropout
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Author(s):
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Xiaohong Yan*+ and W. John Boscardin
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Companies:
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University of California, Los Angeles and University of California, Los Angeles
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Address:
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3170 Sawtelle Blvd., Los Angeles, CA, 90066,
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Keywords:
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longitudinal study ; missing data ; informative dropout ; Bayesian
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Abstract:
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We present a variety of approaches for analyzing data from a clinical trial with incomplete longitudinal data. It is often inappropriate to assume the missingness is at random for such data, especially when dropouts are common. We extend the methodology of Carpenter, Pocock, and Lamm (2002) to simultaneously model multivariate incomplete longitudinal data and the potentially informative dropout (ID) mechanism using a Bayesian approach. The methodology is illustrated through reanalysis of rheumatology clinical trial data from a study of penicillamine treatment for scleroderma patients. We examine two primary longitudinal measures for assessing the outcome of the study. We compare the results for univariate and bivariate informative dropout models to approaches commonly employed, such as complete case analysis, LOCF, and multiple imputation.
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- Authors who are presenting talks have a * after their name.
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